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Introduction, section snippets, references (53), cited by (37).

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More recently, social media platforms such as YouTube and Facebook have enabled people to broadcast live videos that have been used by protesters in many countries to show instances of police brutality,18 creating international pressure discouraging the governments to use excessive force. As a result, their roles have been explored and documented by several studies in lowering corruption and promoting democracy (Enikolopov et al., 2018; Jha and Sarangi, 2017; Diamond, 2010; Jha and Kodila-Tedika, 2020). While the internet and social media have also been subject to censorship by authoritarian governments (Freedom House, 2009), controlling the exchange of information on the internet and censoring social media is much more difficult than controlling traditional media.

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• DOI: 10.3145/EPI.2018.NOV.01
• Corpus ID: 69846074

Social media and democracy

• Homero Gil de Zúñiga , Brigitte Huber , N. Strauss
• Published in El Profesional de la… 5 December 2018
• Political Science

55 Citations

Social media and democratic consolidation in the postmilitarized nations: a study of pakistan, nigeria and brazil, analyzing the role of social media in strengthening democracy in pakistan, how to study democratic backsliding, does social media and democracy work in symbiosis in india, origin and evolution of the news finds me perception: review of theory and effects, from ascriptive to participatory citizenship: social conflict, political belonging, and the liberal nation-state, youth, counter violent extremism and (social) media: a case of pakistan, social media and democracy, exploring the many faces of social media, active audiences and social discussion on the digital public sphere. review article, 94 references, civic political engagement and social change in the new digital age, twenty years of digital media effects on civic and political participation, conversation, disagreement and political participation, social media as a public space for politics: cross-national comparison of news consumption and participatory behaviors in the united states and the united kingdom, why people dual screen political debates and why it matters for democratic engagement, age and the effects of news media attention and social media use on political interest and participation: do social media function as leveller, the effects of digital media on political knowledge and participation in election campaigns, digital democracy: reimagining pathways to political participation, media freedom, political knowledge, and participation, the online citizen: is social media changing citizens’ beliefs about democratic values, related papers.

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Does Social Media Penetration Enhance Democratic Institutions? Evidence from Varieties of Democracy Data

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• Published: 29 March 2024

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• Alex O. Acheampong   ORCID: orcid.org/0000-0002-5462-5466 1 , 2 &
• John Taden 3  

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We examine whether social media enhances democracy using cross-sectional data from 145 countries. We used Facebook penetration as a proxy for social media. Also, based on the complex definition of democracy, high-level indices, such as egalitarian, participatory, liberal, electoral, and deliberative democracies, were used to capture democracy. Our endogeneity-corrected results documented that high social media penetration, on average, enhances all forms of democracy. In descending order, social media penetration has contributed more to enhancing democracy in high-income economies, followed by lower-middle and upper-middle income economies. In low-income economies, social media penetration has a negative effect on democracy indices. We also documented heterogeneity in the findings based on regions. Marginal analysis also revealed that the positive effect of social media on democracy is higher in countries with higher internet penetration. We suggest that with appropriate interventions, policymakers could leverage social media to enhance democratic institutions.

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1 Introduction

Historical perspectives on the drivers of democracy often concerned themselves with factors such as the level of national wealth, state of economic condition, depth of natural capital, level of education, degree of globalization, and rate of urbanization (Acemoglu & Robinson, 2005 ; Barro, 1999 ; Huntington, 1984 ; Lerner, 1958 ; Lipset, 1959 ; Przeworski & Limongi, 1997 ). Others pointed to the relevance of cultural prerequisites necessary for the emergence of a “democratic personality,” a “modern” personality, or a “civic culture” essential to the proliferation of democratic values, beliefs, and norms (Almond & Verba, 1963 ; Arat, 1988 ; Putnam et al., 1994 ). Though the mass media was often not the central mechanism embedded within these theories, Lerner ( 1958 ) pointed to the potential role of the media as a multiplier of the modernization process through which democracies emerge. In line with this literature, the current study examines the impact of social media on democracy against the backdrop of the recent mass proliferation of digital and social media over the past two decades. In fact, by April 2023, nearly five billion people had logged on to various social media platforms, representing approximately 60% of the global population. By 2027, over 70% of the world is expected to be connected through various social media platforms (Ruby, 2023 ). While Facebook currently accounts for more than half of all global social media users, other platforms such as Tiktok, Twitter, Twitch, Mastodon, Clubhouse, and the recently launched Threads are growing at exponential rates and attracting more and more people into a new global reality.

Indeed, this rapid growth of social media has attracted questions regarding its impact on democracy. On the one hand, a cadre of digital optimists contends that social media penetration would have a net positive impact on democracy by, among other mechanisms, lowering costs associated with political engagement, eliminating the information asymmetry between citizens and their political representatives, and empowering the voiceless in society. In line with these optimistic expectations, researchers have recorded extensive evidence of increased political participation across multiple countries. Activities such as participation in protests, engagement in political discourses, and donations to political causes have all risen thanks to the increasing use of social media (Lorenz-Spreen et al., 2023 ). Others have shown that by using aggregate democratic indices, social media strengthens democratic variables around the world (Jha & Kodila-Tedika, 2020 ).

On the other hand, a counterbalancing perspective by a cadre of cyber-pessimists asserts that social media usage would have a detrimental effect on democracy. Social media penetration, they contend, empowers anti-democratic adversarial actors and helps connect fringe voices with disruptive intentions that foster hate, misinformation, and disinformation to undermine trust in democratic institutions. Digital pessimists also argue that social media persists in sorting the mass public into ideological echo chambers that encourage rejection of opposing views, deter political collaboration, and distort a shared sense of reality. In line with these pessimistic expectations, other researchers have shown that social media usage has been central to the rise of populist movements such as those that propelled the 2016 election of Donald Trump, the Brexit vote in the UK, and the eventual attack on the US Capitol on January 6, 2021, that threatened the foundations of the American democracy (Lorenz-Spreen et al., 2023 ; Olaniran & Williams, 2020 ). The extant literature has also disclosed that the growth of social media is closely associated with declining trust in democratic institutions, faltering quality of political discourse, and rising polarization among fellow citizens (Lorenz-Spreen et al., 2023 ; Persily & Tucker, 2020 ).

However, in spite of these instructive insights into the relationship between social media usage and democracy, the discourse is yet to benefit from a holistic analysis of the impact of social media on different forms of democracy across the world. That is, does social media penetration impact all democracies equally? Patently, different democratic systems or principles may be fashioned on different institutional setups, offering different levels and forms of political participation or reflecting different societal objectives. Taking the Varieties of Democracy (V-DEM) classification of democracies into consideration, for example, a democracy built dominantly on participatory principles might offer opportunities for participation vastly different from one built dominantly on egalitarian or liberal principles (Coppedge et al., 2011 , 2018 ). For this reason, there is reason to believe that the impact of social media on democracy will depend significantly on the type of democracy in question. Therefore, this study aims to investigate social media’s impact on democracy across 145 countries.

The study’s novelty and contributions to the literature on the political implications of social media are discussed as follows. First, studies analyzing the relationship between social media and democracy across countries have relied on cross-country measures of democracy proffered by sources such as the Freedom House Index, Eurobarometer, Congressional Cooperative Election Study (CCES), and other regional surveys (See: Placek, 2018 ; Lelkes, 2020 ; Jha & Kodila-Tedika, 2020 ). However, a fundamental challenge of these measures is their inadequacy in “measuring small changes and differences in the quality of autocracy/democracy; empirically analyzing relationships among various elements of democracy; and evaluating the effectiveness of targeted democracy promotion efforts” (Coppedge et al., 2011 , p. 252). Coppedge et al., ( 2011 , 2018 ) also decry the validity, reliability, aggregation, coding, sources, and coverage of these existing measures, disclosing that the indices from these sources are “narrow” as they often only reflect the existence of elections. They stress that democracy transcends the mere presence of elections and that reliance on aggregate measures of democracy might conceal small but relevant changes happening within the sample. Thus, to draw more valid conclusions and offer more reliable policy recommendations, there is a need to seek more precise democracy indices. This paper, thus, addresses this gap by being the first to comprehensively analyze the impact of social media on different forms of democracy, recognizing that democracy is a complex concept with multiple dimensions. In line with the political science literature, our study adopts five high-level democracy indices: electoral, liberal, participatory, deliberative, and egalitarian democracy, as proposed by Coppedge et al. ( 2018 ). These democracy indicators offer a novel approach to comprehending and measuring democracy, providing a comprehensive and detailed dataset that goes beyond the simplistic notion of democracy merely involving elections. The approach contributes to the existing literature and provokes a new perspective crucial to a more contextualized yet simplified understanding of the relationship between social media and democracy.

Second, social media penetration and usage, as well as democracy, are not homogenous across the globe and differ among geographical regions. This study, therefore, contributes to the literature by probing further if the impact of social media on democracy differs across geographical regions and countries at different stages of economic development. Our empirical results supported this claim and highlighted that in low-income economies, social media penetration has a negative effect on egalitarian, participatory, liberal, electoral, and deliberative democracies. On the other hand, social media penetration significantly improves egalitarian, participatory, liberal, electoral, and deliberative democracies in lower-middle, upper-middle, and high-income economies. The findings also indicate that the effect of social media on democracy differs among regions with a significant and positive impact when restricting our study sample to only East Asia & Pacific, America Footnote 1 and MENA countries. However, the findings showed that social media is not a significant determinant of democracy when we restrict the study sample to only sub-Saharan Africa, South Africa, Europe, and Central Asia countries.

Third, unlike previous studies such as Jha and Kodila-Tedika ( 2020) , this study examines if the impact of social media on democracy depends on internet penetration. We argue that social media is a complementary good and that the ability of social media to function effectively depends on other goods, such as internet access. For instance, one cannot effectively log on to facebook without the internet. Our empirical results revealed that the effect of social media on democracy is conditional upon internet penetration. Thus, results confirm that for social media (Facebook) to enhance democracy, there should be higher internet penetration. Therefore, in countries with poor internet access, social media would not enhance democracy and vice versa.

Methodologically, this study relies on the Lewbel two-stage least square estimator to address endogeneity. This external instrument-free estimator constructs its internal heteroskedasticity-based instrument from the auxiliary equation residuals, which is then multiplied by each of the included exogenous variables in a mean-centred form (Lewbel, 2012 ). We also adopted Machado and Silva’s ( 2019 ) method of moment quantile regression (MMQR) technique, which accounts for distributional heterogeneity and fixed effects, to test for the robustness of the effect of social media on the distribution of democracy variables. Finally, we adopted Young and Holsteen's ( 2017 ) analytical approach to evaluate the uncertainty in our model specification and the robustness of the effect of social media on the democracy variables.

The findings from this study would have significant implications for policy. The concerns about the impact of social media usage on democracy are at their highest. Lawmakers in the US and other countries are currently debating bills on platform accountability and transparency to ensure that social media, which has become the de facto public town square, safeguards a healthy and inclusive environment for political debate (Gallo & Cho, 2021 ). Steps are also being taken in countries such as Mexico, Ecuador, Finland, Sweden, Norway, and several other parts of the world to introduce programs to improve citizens’ media and digital literacy to guard against the spectre of disinformation and misinformation (Barredo-Ibáñez et al., 2023 ; Erstad et al., 2021 ). The effectiveness of the eventual regulations and programs would depend significantly on a comprehensive understanding of the impact of social media on various principles and forms of democracy. Further, the knowledge from this study should guide governments and civil society groups to better design policies addressing social media usage’s specific impacts on specific aspects of their democracy.

For the remainder of the paper, we review the literature on the relationship between social media and democracy, describe the methodology, discuss the results, and conclude.

2 Review of Related Literature

The relationship between social media and democracy might draw some of its theoretical roots from the early conceptions of modernization theory. Lerner ( 1958 ) was among some of the first to draw parallels between mass media and certain drivers of democracy by asserting that mass communication might enhance the modernization process that facilitates democratic transitions through education and rapid information dissemination. Lipset ( 1959 ) clarified the modernization theory as the likelihood of societies experiencing democratic transitions as they industrialize, urbanize, and experience rising levels of education. The social prerequisites of this transition are underpinned by widespread literacy and education as well as a large and politically active middle class that supports democratic values and institutions. Indeed, several studies continue to report the positive impacts of factors such as education, urbanization, and income on several political outcomes, such as democracy, the ability to hold leaders accountability, and the desire to vote out non-performing leaders (Barro, 1999 ; Shah, 2011 ; Taden et al., 2023 ). In this context, the mass (social) media then promotes democracy by facilitating the education of citizens and making information available to a larger public.

Indeed, social media has become the leading influencer of democracy and public life, even for people not on social media (Gadjanova et al., 2022 ; Kamau, 2017 ). Digital technology and social media have opened communication links between citizens of the same or different countries at a scale not witnessed before. As a natural outcome of its rapid growth, social media has also become a mainstream platform for forming, shaping, and disseminating political messages, raising several critical questions, not the least among which is its impact on democracy. A functional democracy thrives on communication, consensus building, and mass participation. Extant research portends, therefore, that social media might enhance democracy by increasing political participation. Indeed, social media usage has been found to motivate a range of civic behaviours, from low-effort actions such as liking, commenting on, and sharing political messages to high-cost actions such as protesting under repressive governments (Lorenz-Spreen et al., 2023 ).

Social media has been crucial to the success of protests that generated recent regime and political changes in places such as Sudan and Chile, as well as during the Arab Spring in Tunisia, Libya, Egypt, Yemen, Syria, and Bahrain (Howard & Hussain, 2013 ; Kadoda & Hale, 2015 ; Valenzuela et al., 2012 ). In this vain, social media might speed up democratic transitions by enabling citizens to bypass information barriers established by oppressive regimes. It might also allow more effective political mobilization by connecting fellow citizens with like-minded interests or grievances. Nonetheless, Enikolopov et al. ( 2020 ) reveal that while a 10% increase in social media penetration in Russian cities increases the probability of an anti-government protest by 4.6% and the number of protest participants by 19%, it only does so “by reducing the cost of coordination rather than by spreading information critical of the government,” revealing the power of government to control social media content to its repressive advantage. This is synonymous with how repressive governments use social media to control narratives and suppress critical voices during elections (Abrahamsen & Bareebe, 2016 ; Amoah, 2020 ). Also, while the literature overwhelmingly demonstrates that social media increases general political participation (Lorenz-Spreen et al., 2023 ), Lelkes ( 2020 ) found that internet usage, unfortunately, does not affect pertinent participatory activities such as voter turnout, and information dissemination might only enlarge the subset of the population disenchanted with politics.

Closely related to the benefit of political participation is the positive impact of social media usage on political engagement. Social media usage might help eliminate the information asymmetry between citizens and their political representatives regarding the latter’s actions, leading to an improved alignment of their interests. For instance, in a study of Twitter usage behaviour among US Congressmen, Mousavi and Gu ( 2019 ) discovered that congressmen who use Twitter were more likely to vote in line with the prevailing opinions of their constituents. Additionally, social media usage might help eliminate “class” in favour of equality (Schradie, 2012 ), ensuring that all or more stakeholders are heard and regarded equally.

Democracy requires trust among fellow citizens as well as trust in political and social institutions such as the judiciary, media, and experts (such as scientists and health departments during health crises). However, social media usage might affect democracy by either negatively or positively impacting trust in these institutions. On the positive domain, Placek ( 2018 ) found that social media usage contributes to democratic stability by increasing public trust in the justice system, police, military, and political parties in 11 Central and Eastern European countries. The author also found that social media usage leads to a higher satisfaction with democracy, a stronger national identity, and a more positive attachment to one’s local community. Enikolopov et al. ( 2020 ) also show that social media penetration in Russian cities improves support for the national government, as others have shown from samples in Malaysia (Miner, 2015 ), Kazakhstan (Bekmagambetov et al., 2018 ), and China (Zhou et al., 2020 ).

Contrary to these findings, other studies show that social media might decrease support for democracy or government by depleting trust in institutions or weakening community ties. Sabatini and Sarracino ( 2019 ) reveal that social media access depletes trust in the Italian police, potentially hampering their ability to uphold the rule of law. Bekmagambetov et al. ( 2018 ) also show from a survey of Kazakhstani college students that those “who see, read and share critical information tend to develop distrust in government institutions, which results in an increased proclivity for protest.” It’s worth noting, however, that a decrease in trust for authoritarian governments due to social media access might inadvertently speed up a transition to democracy. For instance, while internet use in China has been found to exacerbate negative public perceptions about the government and amplify scepticism toward government officials, the scepticism, in turn, intensifies public demand for political participation and elevates the expectation of government performance (Zhou et al., 2020 ).

Social media has also been found to affect democracy by increasing or decreasing political knowledge, polarization, and populism. For example, a strand of the literature has found that social media usage increases political knowledge and interest, improving the quality of political engagement in the process (Edgerly et al., 2018 ; Salaudeen & Onyechi, 2020 ). Improved political knowledge might perfect democracy by enhancing citizens’ oversight responsibilities as they are better able to hold leaders accountable. Nevertheless, the news-find-me theory portends that social media usage decreases the quality of knowledge citizens acquire online (Lee, 2020 ). That is, social media users may no longer actively seek out news as they expect to be presented with it. However, as they wait for news to find them, they may be presented with fake news organized to serve those with neither the interest nor the ability to verify its authenticity. Fake news might undermine democratic stability by distorting shared realities, deepening polarisation, amplifying unfounded rage, and undermining trust in institutions and fellow citizens.

Additionally, adversarial foreign actors might seize opportunities presented by social media to interfere in the internal affairs of a country to achieve geopolitical objectives. For instance, the Russian Internet Research Agency, a “troll factory” set up prior to the 2016 US election and Brexit vote in the UK, regularly produced and disseminated pro-Trump and pro- Brexit propaganda that contributed to their eventual outcomes (Persily & Tucker, 2020 , Chapter 2). In other instances, ghost and foreign social media actors might deploy bots to sow disinformation, misinformation, distrust, and hate among fellow citizens, leading to higher internal agitation (Chibuwe, 2020 ). Indeed, amid other ambitions, “undermining democracy has been a strategic objective of Russia’s,” which its government has advanced by using social media to sow dissension and disillusionment in democracy itself while nudging public support for extraconstitutional claims on power in countries such as Mali, Burkina Faso, and Sudan (The Africa Center for Strategic Studies, 2023 ).

Unsurprisingly, several studies report a detrimental association between social media usage and political polarization in South Korea (Lee et al., 2018 ), the United States (Bryson, 2020 ), Germany (Adam et al., 2019 ), and several other parts of the world (Lorenz-Spreen et al., 2023 ). In South Korea, for instance, social media users were found to develop more extreme political attitudes than non-users. This is particularly possible because social media exacerbates the homophily of social and ideological networks as individuals persist in seeking out only those who are similar to them socially and politically, leading to high intolerance for external viewpoints. In Britain and the United States, the excessive polarization of civil society via social-media-created echo chambers and filter bubbles undoubtedly fuelled populist movements that culminated in Brexit , the election of Donald Trump, and the eventual attack on the US Capitol that threatened the foundations of the American democracy (Lorenz-Spreen et al., 2023 ; Olaniran & Williams, 2020 ). Nevertheless, other studies show that social media usage is associated with an increased diversity of viewpoints and political engagement in Kenya, Nigeria, and the United States (Adegbola & Gearhart, 2019 ).

Indeed, social media generates varied outcomes for democracy. As surmised from the literature, in some places, the penetration of social media might, for example, improve political participation but simultaneously suppress electoral turnout as voters grow disenchanted with the political class. In other places, the expansion of social media usage might enhance citizens’ political knowledge but persist in sorting the public into cantankerous ideological echo chambers, increasing intolerance and deterring political collaboration in the process. The dominant implication from the analysis is that, as different democracies have different institutional setups and varied opportunities for public participation, social media usage might impact different aspects of democracy differently and, more contextually, different democracies differently. Expectantly, the impact of social media usage on electoral democracy, for example, might differ from its impact on deliberative democracy. Commonly, as several countries also have democratic systems built on a combination of different democratic principles, a liberal democracy, for example, might be enhanced by the “class-elimination” dagger of social media usage but simultaneously suffer the irreparable wrath of its institutional-trust-destroying axe.

For this purpose, we turn to the V-DEM classification of democracies—a multidimensional and disaggregated classification of the concept of democracy as a system that transcends the mere presence of elections. In line with this classification, we expect that the impact of social media will differ across participatory, liberal, deliberative, egalitarian, and electoral democracies. The V-DEM project describes participatory democracy as one in which citizens get to participate directly in government through local democratic institutions. Liberal democracy prioritizes individual rights and equality before the law. Deliberative democracy gauges the process through which decisions are made in the system and whether dialogue is respectful or coerced. Egalitarian systems are built to neutralize societal imbalances by ensuring equal access to power and resources. Finally, electoral systems focus on making rulers responsive to citizens by ensuring free, fair, and competitive elections. Understandably, the electoral democracy index remains a significant component of all other democracies since there is no democracy without elections (Coppedge et al., 2011 , 2018 ). Inherently, the different democracies or democratic principles strive on different institutional setups, offer varied levels of opportunity for mass participation, and project different societal objectives.

3 Methodology

3.1 empirical model and data.

The primary objective of this study is to examine the impact of social media penetration on different forms of democracy across the globe. Consistent with Jha and Kodila-Tedika ( 2020 ), this study used cross-sectional data from 145 countries Footnote 2 to estimate the effect of social media on varieties of democracy indices. The reduced-form model for estimation is expressed in Eq. ( 1 ).

\({VDEM}_{i}\) denotes the varieties of democracy variables. As a novelty and contribution to the literature, we deploy five (5) unique democracy indices to capture the different notions of democracy as operationalized in the political science literature (Coppedge et al., 2018 ). These democracy indices are electoral, liberal, participatory, egalitarian, and deliberative democracy. We extracted the varieties of the democracy indices from the Coppedge et al. ( 2018 ) varieties of democracy (V-DEM) database.

\({lnSM}_{i}\) represents social media penetration expressed in logarithm. Social media penetration is approximated using the number of Facebook users per 100. The Facebook user data was borrowed from Jha and Kodila-Tedika ( 2020 ) and Kodila-Tedika ( 2021 ), originally developed by Quintly and made publicly available in 2012. Footnote 3

\({X}_{i}\) is a set of control variables included in the empirical model to address variable omission bias. Following the literature on the determinants of democracy, six (6) key variables. We accounted for the effect of GDP per capita (Barro, 1999 ; Heo & Tan, 2001 ; Jha & Kodila-Tedika, 2020 ). According to the modernization theory propounded by Lipset ( 1959 ), GDP per capita plays a very influential role in democratization, and higher economic growth increases democratization. The second variable we controlled in our model is trade openness (Li & Reuveny, 2003 ). We expect trade openness to have an inverse relationship with democracy. This is because literature indicates that trade openness hinders democracy by widening income inequality and subsequently driving political conflicts (López-Córdova & Meissner, 2008 ). Another variable included in the empirical model is natural resources rent (Barro, 1999 ; Brooks & Kurtz, 2016 ). Natural resource rent is argued to undermine democratic institutions because governments use rents from natural resources to support policies that influence public opinion in favour of the ruling class (Aslaksen, 2010 ). We also controlled for the effect of internet penetration (Jha & Kodila-Tedika, 2020 ) and mobile phone penetration (Ben Ali, 2020 ; Fleming, 2002 ). Internet and mobile phone penetration are important for enhancing democracy since they enable people to express their opinions and participate in political debates (Weare, 2002 ). Finally, we accounted for the effect of years of schooling (Barro, 1999 ; Jha & Kodila-Tedika, 2020 ). Education, proxied with years of schooling, is expected to have a positive effect on democracy since education increases civic participation (Glaeser et al., 2007 ). GDP per capita (constant 2015 US$) was used as a proxy for GDP per capita; trade openness is proxied with trade (% of GDP); internet penetration is proxied with secure Internet servers (per 1 million people); mobile phone penetration is represented with mobile cellular subscriptions (per 100 people); years of schooling represented with secondary education, duration (years) and natural resource rent is represented by total natural resources rents (% of GDP). Except for the years of schooling variable, the rest of the control variables were log-transformed before being used for the empirical estimation. All the control variables were retrieved from World Development Indicators.

\({\beta }_{1}\) and \({\beta }_{k}\) are the unknown parameters to be estimated and \({\varepsilon }_{i}\) is the error term.

Table 1 provides descriptive statistics for all the variables used for the empirical analysis. As a cross-sectional study, all the variables considered belong to 2012.

The correlation between social media penetration and the democracy indices is presented in Fig.  1 . Figure  1 shows a stronger positive correlation between social media penetration and democracy indices, indicating that increasing social media penetration increases all forms of democracy. While this bivariate correlation yields some insight into the relationship between social media penetration and democracy, we used econometric techniques to unravel the effect of social media on democracy, considering the effect of other variables.

Bivariate relationship between social media and democracy indices

3.2 Econometric Estimation Strategies

Similar to the study of Jha and Kodila-Tedika ( 2020 ), we estimated the baseline results using the ordinary least square (OLS) estimator. One of the weaknesses of OLS is its inability to handle endogeneity that might emerge from measurement error, reverse causality, or variable omission bias. Our model’s first source of endogeneity is measurement error in social media penetration. Quintly, the organization responsible for Facebook user data, deployed an advertising tool that belongs to the Facebook corporation to obtain data on Facebook users across countries (Kodila-Tedika, 2021 ). Despite using advertising tools belonging to Facebook, there seems to be an error regarding the data on the number of Facebook users across countries provided by Quintly. For instance, Kodila-Tedika ( 2021 , p. 129) indicate that the data on Facebook users provided by Quintly should be interpreted with caution since Facebook claims that the data provided by Quintly is slightly different from the official number of Facebook users provided by Facebook. Despite the measurement error, the Quintly data on Facebook users have become the only available dataset that has been used in a number of studies, including Asongu et al. ( 2019 ), Jha and Kodila-Tedika ( 2020 ), Kodila-Tedika ( 2021 ) and Jha and Sarangi ( 2017 ). Another source of endogeneity is the reverse causality between democracy and social media penetration. While we have discussed the one-way effect of social media on democracy in the literature review section, democracy could also impact social media. At the core of democratic institutions is the freedom of expression. Social media is also a channel through which people can express their views on relevant national policy discourse. This indicates that democracy could increase social media penetration because of its fundamental principle of freedom of speech and expression. The failure to address these endogeneity sources could lead to a downward bias in the OLS estimates.

In relation to the above discussion, we adopted the instrumental variable regression technique to address endogeneity. Given that getting a reliable external instrument for identification was challenging, we specifically applied the Lewbel ( 2012 ) two-stage least squares technique to handle endogeneity issues as stated in Eqs. (2) and (3).

where \({VDEM}_{i}\) denotes the democracy variables and \({lnSM}_{i}\) represents social media penetration. \(U\) denotes unobserved characteristics that affect democracy and social media penetration. \({V}_{1}\) and \({V}_{2}\) are the idiosyncratic error terms. The Lewbel two-stage least squares technique involves taking vector \(Z\) of the observed exogenous covariates and applying \([Z-E\left(Z\right)]{\xi }_{2}\) as an instrument, provided that:

There is heteroskedasticity in \({\xi }_{j}\) . The vector \(Z\) may be a subset of \(X\) or equal to \(X\) . Given that \({\xi }_{2}\) is population parameter and it cannot be observed directly, we use the estimate of the sample parameter from the first stage equation (Eq.  3 ) and use the vector \([Z-E\left(Z\right)]{\xi }_{2}\) as the instrument. Generally, the Lewbel two-stage least square estimator is an external instrument-free estimator that constructs its internal heteroskedasticity-based instrument from the auxiliary equation residuals, which is then multiplied by each of the included exogenous variables in a mean-centred form (Lewbel, 2012 ). The Lewbel ( 2012 ) two-stage least square estimator yields efficient and consistent estimates robust to endogeneity and heteroskedasticity.

4 Results and Discussion

4.1 baseline results estimated with ols.

The baseline results estimated using OLS are presented in Table  2 . Table 2 depicts that irrespective of the specification, social media has an insignificant relationship with egalitarian democracy [Columns 9 and 10]. However, social media has a positive and statistically significant relationship to other forms of democracy, such as electoral, liberal, participatory and deliberative democracy, at a 1% or 5% significance level. Specifically, the estimated coefficient of the effect of social media on electoral democracy is 0.051 in Column 1 when we controlled for only internet and mobile phone penetrations and 0.045 in Column 2 when we further controlled for other control covariates such as GDP per capita, natural resources rent, years of schooling and trade openness. Consistently, the estimated effect of social media on liberal democracy is 0.036 in Column 3 and 0.032 in Column 4. Also, the estimated effect of social media on participatory democracy is 0.032 in Column 5 and 0.027 in Column 6. In Column 7, the estimated coefficient of the effect of social media on deliberative democracy is 0.034, and it is 0.031 in Column 8. These findings suggest that social media penetration significantly enhances democratic institutions. Social media improves democracy by promoting electoral competition, facilitating citizens’ and organizations’ participation in political process and public decision-making, promoting equality in rights and freedoms, and decentralization of political power ( for instance, seeLorenz-Spreen et al., 2023 ; Mousavi & Gu, 2019 ; Schradie, 2012 ). These findings support Jha and Kodila-Tedika’s ( 2020 ) empirical finding that social media has a significant positive relationship with Polity IV.

Many studies, including Campante et al. ( 2017 ) and Gavazza et al. ( 2018 ), have shown that internet penetration negatively affects electoral democracy. Others, such as Falck et al. ( 2014 ), have indicated that internet penetration plays no significant role in electoral democracy. However, our results affirm that internet penetration has a positive and statistically significant effect on the democracy indices. The estimated coefficients show that internet penetration increases electoral democracy by 0.048 to 0.072, liberal democracy by 0.065 to 0.086, participatory democracy by 0.044 to 0.065, deliberative democracy by 0.055 to 0.069 and egalitarian democracy by 0.069 to 0.079. Also, the results indicate that mobile phone penetration negatively affects democracy variables. However, the impact is statistically insignificant in models that account for GDP per capita, natural resources rent, years of schooling and trade openness, suggesting that these variables could mediate the effect of mobile penetration on democracy. In the baseline models, mobile phone penetration reduces electoral democracy by 0.131, liberal democracy by 0.128, participatory democracy by 0.107, deliberative democracy by 0.104 and egalitarian democracy by 0.096.

The results show that GDP per capita has a statistically significant negative relationship with electoral, liberal, and participatory democracy. The results imply that an increase in GDP per capita is associated with a 0.071 reduction in electoral democracy, 0.005 reduction in liberal democracy and 0.053 reduction in participatory democracy. The negative relationship between GDP and the democracy variables could arise from the role of high GDP per capita in social inequality. Rising social and income inequalities due to GDP per capita could increase social tensions and political instability, consequently destabilizing democratic institutions. This result differs from Barro ( 1999 ) and Heo and Tan ( 2001 ), who found a positive relationship between GDP per capita and democracy. Contrarily, GDP per capita has a statistically insignificant relationship with deliberative and egalitarian democracy. These results indicate that the impact of GDP per capita on democracy depends on how democracy is conceptualized and measured. Previous studies, including Csordás and Ludwig ( 2011 ) and Jha and Kodila-Tedika ( 2020 ), documented that GDP per capita does not explain democracy.

Consistent with the political “resources curse theory” and the findings of Barro ( 1999 ) and Sarah M Brooks and Kurtz ( 2022 ), our finding confirms that natural resources rent has a statistically significant negative relationship with electoral, participatory, deliberative, and egalitarian democracy. The results imply that natural resources rent is associated with a 0.026 reduction in electoral democracy, 0.017 reduction in participatory democracy, 0.023 reduction in deliberative democracy and 0.018 reduction in egalitarian democracy. However, natural resources have an insignificant effect on liberal democracy. Consistent with Li and Reuveny’s ( 2003 ) findings, the results in Table  2 highlight that trade openness has a statistically significant negative relationship with electoral, liberal, participatory, deliberative, and egalitarian democracy. The results imply that trade openness is associated with a 0.139 reduction in electoral democracy, 0.147 reduction in liberal democracy, 0.139 reduction in participatory democracy, 0.147 reduction in deliberative democracy and 0.096 reduction in egalitarian democracy. Also, similar to the findings of Jha and Kodila-Tedika ( 2020 ), the estimates indicate that years of schooling have a statistically insignificant effect on the democracy variables, indicating that schooling is not a key determinant of democracy.

4.2 Endogeneity-Corrected Results

The section reports the endogeneity-corrected results from the Lewbel 2SLS estimator. The Lewbel 2SLS results are presented in Table  3 . Table 3 shows that across all the specifications, social media has a positive and statistically significant effect on the democracy indices at a 1% level. The Lewbel 2SLS coefficients are relatively higher than the OLS estimates. For instance, The Lewbel 2SLS estimates suggest that social media increases electoral democracy by 0.075 in Column 1 and 0.090 in Column 2. Also, social media increases liberal democracy by 0.061 in Column 3 and 0.072 in Column 4. At the same time, social media increases participatory democracy by 0.047 in Column 5 and 0.061 in Column 6. In Column 7, the estimated coefficient of the effect of social media on deliberative democracy is 0.062, and it is 0.069 in Column 8. Finally, social media penetration increases egalitarian democracy by 0.034 in Column 9 and 0.049 in Column 10. These results consistently suggest that social media penetration improves democratization even after accounting for endogeneity.

Both the OLS and the Lewbel 2SLS results support the notion that social media penetration is key for enhancing democracy. However, the OLS and the Lewbel 2SLS estimates (coefficients) on the social media variables are relatively small compared to what is established in a closely related study. For instance, Jha and Kodila-Tedika ( 2020 ), in their study, established from OLS that estimates (coefficients) of the effect of social media (Facebook penetration) on Polity IV range between 0.412 to 0.650 and from two-stages least square (instrumenting internet penetration), the estimated coefficients on the effect of social media on Polity IV ranges between 0.437 to 0.821. The size of these estimated coefficients from Jha and Kodila-Tedika’s ( 2020 ) study is larger than that of the estimated coefficients established in this study. This analysis proves that even if social media statistically improves the different forms of democracy considered in this study, the effect (size of the coefficients) is relatively small.

The relationship between the control variables and democracy based on the Lewbel 2SLS technique is not qualitatively different from the OLS results. For instance, the Lewbel 2SLS consistently shows that internet penetration has a positive and statistically significant effect on the democracy indices at a 1% level. Also, mobile phone penetration consistently has negative and statistically significant negative effects on the democracy variables across all specifications. Further, natural resources and trade openness have a statistically significant negative impact on the democracy indices. The Lewbel 2SLS estimates indicate that both GDP per capita and years of schooling have a statistically insignificant effect on the democracy variables.

4.3 Does the Effect of Social Media on Democracy Differ Across Income Groups?

As presented in Appendix Figure 5 , the democracy indices differ among countries at different stages of economic development. Generally, Appendix Figure 5 shows that democracy is higher in high-income, upper-middle-income, lower-middle-income, and, lastly, low-income countries. In this section, we examine whether social media penetration’s impact on democracy differs across countries at different stages of economic development. We estimated the income group models using the Lewbel IV-2SLS estimator, and the results are presented inFig.  2 . Footnote 4 Figure  2 shows the effect of social media on the democracy variables across countries at different stages of economic development while accounting for GDP per capita, trade openness, natural resources rent, internet penetration, years of schooling, and mobile phone penetration.

Lewbel IV-2SLS regression coefficients of the effect of social media on democracy variables (Lewbel IV-2SLS estimates and 90% confidence interval) across income groups. All regression models include control variables (GDP per capita, trade openness, natural resources rent, internet penetration, years of schooling, and mobile phone penetration)

Figure  2 Panel A suggests that social media has a positive and statistically significant effect on electoral democracy in lower-middle, upper-middle and high-income economies, while it has an insignificant negative effect on electoral democracy in low-income economies. Social media increases electoral democracy by 0.212 in lower-middle income countries, 0.101 in upper-middle income countries and 0.248 in high-income economies. Figure  1 Panel B suggests that social media has a positive and statistically significant effect on liberal democracy in lower-middle, upper-middle and high-income economies, while it has an insignificant negative effect on electoral democracy in low-income economies. Social media increases liberal democracy by 0.213 in lower-middle income countries, 0.063 in upper-middle income countries and 0.252 in high-income economies

Figure  2 Panel C suggests that social media has a positive and statistically significant effect on participatory democracy in lower-middle, upper-middle and high-income economies. Social media increases participatory democracy by 0.117 in lower-middle income countries, 0.067 in upper-middle income countries and 0.239 in high-income economies. At the same time, social media has a statistically significant negative effect on participatory democracy in low-income economies, with an estimated coefficient of 0.239. Figure  2 Panel D suggests that social media has a positive and statistically significant effect on deliberative democracy in lower-middle, upper-middle, and high-income economies, while it has an insignificant negative effect on deliberative democracy in low-income economies. Social media increases deliberative democracy by 0.219 in lower-middle income countries, 0.054 in upper-middle income countries and 0.262 in high-income economies. Finally, Fig.  2 Panel E suggests that social media has a positive and statistically significant effect on egalitarian democracy in lower-middle, upper-middle and high-income economies, while it has an insignificant negative effect on egalitarian democracy in low-income economies. Social media increases electoral democracy by 0.152 in lower-middle income countries, 0.044 in upper-middle income countries and 0.200 in high-income countries

Generally, the evidence suggests that social media penetration enhances democracy in high-income economies, followed by lower-middle and upper-middle income economies. However, in low-income economies, social media penetration has a negative effect on democracy indices. The role of social media in limiting democratization in low-income countries could be attributed to the lower penetration of social media. However, in high-income, upper-middle-income, and lower-middle-income countries, higher social media penetration contributes significantly to democracy. As depicted in Appendix Figure 6 , social media penetration is lowest in low-income countries, while social media penetration is higher in high-income countries, followed by upper-middle-income countries and lower-middle income countries. The results of this income group analysis contradict Jha and Kodila-Tedika’s ( 2020 ) finding that social media penetration increases democracy in low-income countries.

4.4 Does the Effect of Social Media on Democracy Differ Across Regions?

We further extend the analysis to examine the impact of social media penetration on democracy across regions. Appendix Figures 7 and 8 highlight that democracy and social media penetration are not homogenous across geographical regions. We estimated the regional results using the Lewbel IV-2SLS estimator, and the results are presented inFig.  3 . Footnote 5 Figure  3 provides a pictorial presentation of the effect of social media on the democracy variables across different regional groups while accounting for GDP per capita, trade openness, natural resources rent, internet penetration, years of schooling, and mobile phone penetration. We estimated one model by restricting the sample to only South Asia and Sub-Saharan Africa countries since countries in these regions, on average, have relatively the lowest social medial penetration (see Appendix Figure 8 ). We also estimated another model by restricting the sample to only Europe and Central Asia countries because, on average, countries in these regions have relatively higher social media penetration (see Appendix Figure 8 ). Finally, we estimated another model by restricting the sample to other regions, including East Asia & Pacific, America (which involves Canada and Latin America & Caribbean countries) and the MENA countries.

Lewbel IV-2SLS regression coefficients of the effect of social media on democracy variables (Lewbel IV-2SLS estimates and 90% confidence interval) across geographical regions. All regression models include control variables (GDP per capita, trade openness, natural resources rent, internet penetration, years of schooling, and mobile phone penetration)

Figure  3 Panel A suggests that social media positively affects electoral democracy in South Asia, Sub-Saharan Africa, and other regions; however, the impact is statistically significant in other regions with an estimated effect of 0.061. In Europe and Central Asia, social media has an insignificant negative effect on electoral democracy. Figure  3 Panel B suggests that social media positively affects liberal democracy in South Asia, Sub-Saharan Africa, and other regions; however, the impact is statistically significant in other regions with an estimated effect of 0.038. In Europe and Central Asia, social media has an insignificant negative effect on liberal democracy. Figure  3 Panel C suggests that social media positively affects participatory democracy in South Asia, Sub-Saharan Africa, and other regions; however, the impact is statistically significant in other regions with an estimated effect of 0.037. In Europe and Central Asia, social media has an insignificant negative effect on participatory democracy. Figure  3 Panel D suggests that social media positively affects deliberative democracy in South Asia, Sub-Saharan Africa, and other regions; however, the impact is statistically significant in other regions with an estimated effect of 0.032. In Europe and Central Asia, social media has an insignificant negative effect on deliberative democracy. Figure  3 Panel E suggests that social media positively affects egalitarian democracy in South Asia, Sub-Saharan Africa, and other regions; however, the impact is statistically significant in other regions with an estimated effect of 0.026. In Europe and Central Asia, social media has an insignificant negative effect on egalitarian democracy

In summary, the regional analysis implies that social media is not a significant determinant of democracy in sub-Saharan Africa, South Africa, Europe, and Central Asia countries. However, the findings highlighted that East Asia & Pacific, America and the MENA countries have benefited politically from social media penetration. In other words, social media penetration has enhanced electoral, egalitarian, participatory, liberal, and deliberative democracies in countries in East Asia & Pacific, America and the MENA regions.

4.5 Does the Effect of Social Media on Democracy Depend on Internet Penetration?

We argue that social media is a complementary good, indicating that the ability of social media to function effectively depends on other goods, such as internet access. One cannot effectively log on to Facebook without the internet. Therefore, we hypothesize that social media’s impact on democracy depends on internet penetration. Table 4 shows the interactive effect of social media and internet penetration on democracy. In Table  4 , the unconditional effect shows that social media has an insignificant effect on electoral, liberal, participatory, deliberative, and egalitarian democracies. At the same time, internet penetration has a statistically significant positive effect on electoral, liberal, participatory, deliberative, and egalitarian democracies. We evaluated the marginal effect of social media penetration on the democracy variables conditioned at different levels of internet penetration using Eq. ( 5 ):

where \({\beta }_{1}\) represents social media coefficients and \({\delta }_{1}\) is the coefficient of the interaction term [social media × internet penetration]. We evaluated the marginal effect of social media at the minimum (−2.546), mean (3.362) and maximum (8.487) values of internet penetration.

The marginal effect of social media penetration suggests that, at higher values of internet penetration, social media has a positive and statistically significant effect on electoral, liberal, participatory, deliberative, and egalitarian democracies. For instance, the marginal effects indicate that at the minimum value of internet penetration, social media has a negative effect on electoral, liberal, participatory, deliberative, and egalitarian democracies; however, the impact is only significant in the egalitarian democracy model. However, at the mean and maximum values of internet penetration, social media has a positive and statistically significant effect on electoral, liberal, participatory, deliberative, and egalitarian democracies. The estimated effect suggests that at the mean value of internet penetration, social media increases electoral democracy by 0.067, liberal democracy by 0.055, participatory democracy by 0.046, deliberative democracy by 0.052, and egalitarian democracy by 0.037. Also, at the maximum value of internet penetration, social media increases electoral democracy by 0.128, liberal democracy by 0.119, participatory democracy by 0.100, deliberative democracy by 0.113, and egalitarian democracy by 0.108. These conditional effect results affirm that for social media (Facebook) to enhance democracy, there should be higher internet penetration. Therefore, in countries with poor internet access, social media would not enhance democracy and vice versa.

4.6 Accounting for the Distribution of Democracy Variables

Appendix Figure 9 shows that the democracy variables are not normally distributed. We further conducted a robustness check by examining the effect of social media on the democracy variable by considering the distributional properties of the democracy variables. We adopted Machado and Silva’s ( 2019 ) method of moment quantile regression (MMQR), which accounts for distributional heterogeneity and fixed effects, to estimate the effect of social media on the distribution of democracy variables. Table 5 presents the MMQR results. Table 5 shows that social media has a statistically insignificant effect on the lower quantile (0.1 quantile) of electoral democracy; however, social media has a positive and statistically significant effect on the mean and higher quantile (0.5 and 0.9 quantile) of electoral democracy. Similarly, social media has an insignificant effect on the lower quantile (0.1 quantile) of liberal democracy; however, social media has a positive and statistically significant effect on liberal democracy at the higher quantile (0.5 and 0.9 quantile). Social media has a neutral impact on participatory democracy at the lower quantile (0.1 quantile); however, social media has a positive and statistically significant effect on participatory democracy at the higher quantile (0.5 and 0.9 quantile). Social media also has an insignificant effect on deliberative democracy at the lower quantile (0.1 quantile); however, social media has a positive and statistically significant effect on deliberative democracy at the higher quantile (0.5 and 0.9 quantile). Finally, social media penetration only has a statistically significant positive effect on egalitarian democracy at the higher quantiles but insignificant on egalitarian democracy at the 0.1 and 0.5 quantiles. These findings generally support that social media improves democracy.

4.7 Testing for the Model’s Robustness and Influence

In this section, we test for the model uncertainty and the robustness of the results to our model specifications. We, therefore, follow Young and Holsteen’s ( 2017 ) analytical approach to evaluate the model uncertainty and the robustness of the effect of social media on the democracy variables. The details of the model uncertainty and the robustness statistics are presented in Appendix Tables 7 , 8 , 9 , 10 and 11 . As presented in Appendix Tables Tables 7 , 8 , 9 , 10 and 11 , 64 unique combinations of the control variables exist. The modelling distribution displayed by Panel A–E of Fig.  4 is the estimated coefficients of social media stored during the estimation of these 64 (2 6  = 64) unique models. As displayed in Fig.  1 , the estimated coefficients on the effect of social medial penetration on electoral, liberal, participatory, egalitarian, and deliberative democracy are positive and significant in every combination of the control variables. In Appendix Table 7 , the robustness ratio is 2.3901, indicating that social media has a stronger robust effect on electoral democracy, which agrees with the 100% sign and significant rate. Also, robustness ratios of 1.7258 (see Appendix Table 8 ), 1.8520 (see Appendix Table 9 ) and 1.7493 (see Appendix Table 10 ) suggest that social media penetration has a strong robust effect on liberal, participatory, and deliberative democracy, which agrees with their respective sign and significant rate. On the other hand, the robustness ratio of 1.0771 in Appendix Table 11 suggests that social media has a weaker robust effect on egalitarian democracy, which agrees with the 50% sign and significant rate. In summary, social media penetration has a robust positive effect on electoral, liberal, participatory, egalitarian, and deliberative democracy, indicating that social media penetration is important for improvement in democratic institutions across the globe.

Modeling distribution of the effect of social media penetration on the democracy variables

In Appendix Table 7 , the model influence statistics indicates that all things being equal, controlling for internet penetration, natural resources rent and GDP per capita reduce the effect of social media penetration on electoral democracy by 0.0418 (63.7%), 0.0096 (14.6%), and 0.0076 (11.6%) respectively. Contrarily, mobile phones, trade openness and years of school increase the effect of social media penetration on electoral democracy by 0.0151 (22.9%), 0.0017 (2.5%) and 0.0002 (0.3%), respectively. Also, in Appendix Table 8 , controlling for the effect of internet penetration, GDP per capita, and natural resources reduce the effect of social media penetration on liberal democracy by 0.0555 (95%), 0.0147 (25.2%) and 0.0094 (16.2%) respectively while mobile phone, trade openness and years of schooling increase the effect of social media penetration by 0.0133 (22.8%), 0.0016 (2.7%) and 0.0002 (0.3%).

As presented in Appendix Table 9 , controlling for internet penetration, GDP per capita and natural resources rent reduce the effect of social media penetration on participatory democracy by 0.0393 (85.9%), 0.0087 (19.1%), and 0.0076 (16.6%), respectively. Mobile phones, trade openness, and years of school increase the effect of social media penetration by 0.0122 (26.8%), 0.0016 (3.6%), and 0.0006 (1.4%), respectively. In Appendix Table 10 , internet penetration, GDP per capita, natural resources rent, and years of schooling reduce the effect of social media penetration on deliberative democracy by 0.0454 (85.3%), 0.0119 (22.3%), 0.0092 (16.6%) and 0.0002 (0.4%) respectively while mobile phone and trade openness increase the effect of social media penetration by 0.0113 (21.1%), and 0.0016(3.1%). Finally, in Appendix Table 11 , internet penetration, GDP per capita and natural resources rent reduce the effect of social media penetration on egalitarian democracy by 0.0571 (152.0%), 0.0177 (47.2%), and 0.0094 (25%) respectively while mobile phone, trade openness and years of school increase the effect of social media penetration by 0.0109 (29.1%), 0.0013 (3.3%) and 0.0008(2.2%) respectively.

5 Conclusion and Policy Implications

The contribution of social media to democratic institutions is still contentious in the political science literature. While some scholars argue theoretically that social media is useful for enhancing democracy, others claim that social media hinders democratic institutions. In view of this theoretical inconsistency, we contribute to the literature by providing empirical evidence on whether social media enhances democracy using cross-sectional data from 145 countries. We used Facebook penetration as a proxy for social media. As a novelty and contribution to the literature, we captured democracy using varieties of high-level and multidimensional democracy indices, such as egalitarian, participatory, liberal, electoral, and deliberative democracies. In this study, we applied the Lewbel two-stage least square estimator to address endogeneity and further deployed the method of moment quantile regression to test the robustness of the results. After applying the Lewbel two-stage least square estimator to address endogeneity, the key findings that emerged from this study are summarized as follows:

First, the findings showed that social media penetration significantly improves varieties of high-level democracy indices such as egalitarian, participatory, liberal, electoral, and deliberative democracies. Second, the findings highlighted that the impact of social media penetration on democracy differs across countries at different stages of economic development. For instance, in low-income economies, social media penetration has a negative effect on egalitarian, participatory, liberal, electoral, and deliberative democracies. On the other hand, social media penetration significantly improves egalitarian, participatory, liberal, electoral, and deliberative democracies in lower-middle, upper-middle, and high-income economies. Third, the findings revealed that social media significantly improves all the forms of democracy when we restrict the study sample to only East Asia & Pacific, America and the MENA region. On the other hand, social media penetration insignificantly affects all forms of democracy if the study sample is restricted to South Asia, sub-Saharan Africa, Europe, and Central Asia only. Fourth, the moderation and marginal effect analysis revealed that internet penetration conditions the effect of social media penetration, indicating that at the mean and maximum values of internet penetration, the positive effect of social media on the democracy variables increases. In contrast, at the minimum value of internet penetration, social media reduces democracy. Finally, in evaluating the model uncertainty and the robustness of the effect of social media, the findings showed that social media penetration has a robust positive effect on electoral, liberal, participatory, egalitarian, and deliberative democracy.

The policy implications of these findings are discussed as follows. The findings suggest that countries could leverage social media to enhance their democratic institutions. In this regard, policies that facilitate easy penetration and usage of social media enhance democracy. The findings also highlighted that the impact of social media on democracy also depends on internet penetration and that when internet penetration is low, social media reduces democracy; however, at higher internet penetration, social media could substantially improve democratic institutions. This finding highlights that policy measures that would increase internet usage while minimizing the cost of internet usage would benefit democracy. While global internet access has improved globally; however, most internet users stay offline. As highlighted in the Global Connectivity 2022 report, one in three people who could go online choose not to stay offline because of the higher cost of internet usage, inability to use the internet, lack of awareness and lack of access to connectivity devices. Making internet costs affordable to scale up internet subscription and usage requires policy measures that subsidize internet service providers’ activities and reduce tariffs on importing internet infrastructures. In addition, policy measures that enhance digital literacy and ensure sustainable energy supply to power internet infrastructures could facilitate internet penetration and usage. Improving internet usage also requires policies that could make the telecommunication sector more competitive since competition in the telecommunication sector could enable internet providers to provide different internet subscription plans at a more affordable cost and meet the needs of different internet consumers.

Despite the uniqueness and contribution of this paper to the discussion of the political implications of social media penetration, our findings should be interpreted with some caveats. Our study is limited by only focusing on the contribution of Facebook penetration on different forms of democratic institutions across the globe. However, other social media platforms, such as Twitter, WhatsApp, Tiktok, Twitch, Mastodon, Clubhouse, and others, could equally influence democracy across the globe. The political implications of these other social media platforms are not considered in this study because of the difficulty in accessing official data on the number of people who  use these social media platforms. We recommend future studies to evaluate the political implications of these social media platforms once data becomes available. Also, it was challenging to access time series data for social media variables, limiting our study to utilize a cross-sectional approach. We, therefore, recommend future studies to deploy time series and panel data techniques to re-examine the linkage between social media and political institutions. Finally, while this study mainly examined the effect of social media on political institutions (democracy), future studies will contribute to the literature if they examine the role of political institutions on social media usage across the globe. Notwithstanding these limitations, our study has contributed to the ongoing debate on the political implications of social media penetration with its novel analytical approaches.

See Tables 6 , 7 , 8 , 9 , 10 and 11 ; Figs. 5 , 6 , 7 , 8 and 9 .

Democracy across income groups

Facebook penetration across income groups

Democracy across regional groups

Facebook penetration across regional groups

Distribution of the democracy variables

America used in this study includes Canada and Latin America & Caribbean countries.

The study sample is presented in Appendix Table 6 .

Currently, one needs to purchase the data from Quintly.

We presented the coefficients for social media using graphs in other to conserve space and provide a pictorial presentation of the income groups. However, the extensive tables containing the estimates for the social media variable and the control variables on the democracy variables across the income group would be made available upon request.

We presented the coefficients for social media using graphs in other to conserve space and provide a pictorial presentation of the regional groups. However, the extensive tables containing the estimates for the social media variable and the control variables on the democracy variables across the regions would be made available upon request.

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We sincerely thank Oasis Kodila-Tedika for sharing the social media data with us. The authors are also grateful to the Journal Editors and the two anonymous reviewers for the valuable comments that helped improve this paper's quality. Nevertheless, the authors are responsible for all remaining errors.

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A systematic review of worldwide causal and correlational evidence on digital media and democracy

• Philipp Lorenz-Spreen   ORCID: orcid.org/0000-0001-6319-4154 1   na1 ,
• Lisa Oswald   ORCID: orcid.org/0000-0002-8418-282X 2   na1 ,
• Stephan Lewandowsky   ORCID: orcid.org/0000-0003-1655-2013 3 , 4 &
• Ralph Hertwig   ORCID: orcid.org/0000-0002-9908-9556 1  

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One of today’s most controversial and consequential issues is whether the global uptake of digital media is causally related to a decline in democracy. We conducted a systematic review of causal and correlational evidence ( N  = 496 articles) on the link between digital media use and different political variables. Some associations, such as increasing political participation and information consumption, are likely to be beneficial for democracy and were often observed in autocracies and emerging democracies. Other associations, such as declining political trust, increasing populism and growing polarization, are likely to be detrimental to democracy and were more pronounced in established democracies. While the impact of digital media on political systems depends on the specific variable and system in question, several variables show clear directions of associations. The evidence calls for research efforts and vigilance by governments and civil societies to better understand, design and regulate the interplay of digital media and democracy.

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The ongoing heated debate on the opportunities and dangers that digital media pose to democracy has been hampered by disjointed and conflicting results (for recent overviews, see refs. 1 , 2 , 3 , 4 ). Disagreement about the role of new media is not a novel phenomenon; throughout history, evolving communication technologies have provoked concerns and debates. One likely source of concern is the dual-use dilemma, that is, the inescapable fact that technologies can be used for both noble and malicious aims. For instance, during the Second World War, radio was used as a propaganda tool by Nazi Germany 5 , whereas allied radio, such as the BBC, supported resistance against the Nazi regime, for example, by providing tactical information on allied military activities 6 , 7 . In the context of the Rwandan genocide, radio was used to incite Rwandan Hutus to massacre the country’s Tutsi minority 8 . In the aftermath of the genocide, using the same means to cause different ends, the radio soap opera ‘Musekeweya’ successfully reduced intergroup prejudice in a year-long field experiment 9 , 10 .

Digital media appears to be another double-edged sword. On the one hand, it can empower citizens, as demonstrated in movements such as the Arab Spring 11 , Fridays for Future and #MeToo 12 . On the other hand, digital media can also be instrumental in inciting destructive behaviours and tendencies such as polarization and populism 13 , as well as fatal events such as the attack on the United States Capitol in January 2021. Relatedly, the way political leaders use or avoid digital media can vary greatly depending on the political context. Former US President Trump used it to spread numerous lies ranging from claims about systematic voter fraud in the 2020 presidential election to claims about the harmlessness of Covid-19. In spring 2022, Russian President Putin had banned most social media platforms that would bypass the state-controlled classical media, probably to prevent access to information about his army’s attack on Ukraine 14 . At the same time, Ukrainian President Zelensky has skilfully used social media to boost Ukrainian morale and engage in the information war with Russia. Examples of the dual-use dilemma of digital media abound.

Clearly, digital media can foster liberation, democratization and participation, but can also play an important role in eroding democracy. The role of digital media is further complicated because unlike other communication technologies, it enables individuals to easily produce and disseminate content themselves, and offers largely frictionless interaction between users. These properties have not only moved the self-organized political behaviour of citizens into the spotlight 15 , but have also shifted power to large digital media platforms. Unlike broadcasters, digital media platforms typically do not create content; instead, their power lies in providing and governing a digital infrastructure. Although that infrastructure could serve as an online public sphere 16 , it is the platforms that exert much control over the dynamics of information flow.

Our goal is to advance the scientific and public debate on the relationship between digital media and democracy by providing an evidence-based picture of this complex constellation. To this end, we comprehensively reviewed and synthesized the available scientific knowledge 17 on the link between digital media and various politically important variables such as participation, trust and polarization.

We aimed to answer the pre-registered question “If, to what degree and in which contexts, do digital media have detrimental effects on democracy?” (pre-registered protocol, including research question and search strategy, at https://osf.io/7ry4a/ ). This two-stage question encompasses, first, the assessment of the direction of effects and, second, how these effects play out as a function of political contexts.

A major difficulty facing researchers and policy makers is that most studies relating digital media use to political attitudes and behaviours are correlational. Because it is nearly impossible to simulate democracy in the laboratory, researchers are forced to rely on observational data that typically only provide correlational evidence. We therefore pursued two approaches. First, we collected and synthesized a broad set of articles that examine associations between digital media use and different political variables. We then conducted an in-depth analysis of the small subset of articles reporting causal evidence. This two-step approach permitted us to focus on causal effects while still taking the full spectrum of correlational evidence into account.

For the present purpose, we adopted a broad understanding of digital media, ranging from general internet access to the use of specific social media platforms, including exposure to certain types of content on these platforms. To be considered as a valid digital media variable in our review, information or discussion forums must be hosted via the internet or need to describe specific features of online communication. For example, we considered the online outlets of traditional newspapers or TV channels as digital source of political information but not the original traditional media themselves. We provide an overview of digital media variables present in our review sample in Fig. 1d and discriminate in our analyses between the two overarching types of digital media: internet, broadly defined, on the one hand and social media in particular on the other hand.

a , Combinations of variables in the sample: digital media (A), political variables (B) and content features such as selective exposure or misinformation (C). Numbers in brackets count articles in our sample that measure an association between variables. b , Geographic distribution of articles that reported site of data collection. c , d , Distribution of measurements (counted separately whenever one article reported several variables) over combinations of outcome variables and methods ( c ) and over combinations of outcome variables and digital media variables ( d ).

We further aimed to synthesize evidence on a broad spectrum of political attitudes and behaviours that are relevant to basic democratic principles 18 . We therefore grounded our assessment of political variables in the literature that examines elements of modern democracies that are considered essential to their functioning, such as citizens’ basic trust in media and institutions 19 , a well-informed public 20 , an active civil society 21 , 22 and exposure to a variety of opinions 23 , 24 . We also included phenomena that are considered detrimental to the functioning of democracies, including open discrimination against people 25 , political polarization to the advantage of political extremists and populists 26 and social segregation in homogeneous networks 23 , 27 .

The political variables in focus are themselves multidimensional and may be heterogeneous and conflicting. For example, polarization encompasses partisan sorting 28 , affective polarization 29 , issue alignment 30 , 31 and a number of other phenomena (see ref. 32 for an excellent literature review on media effects on variations of ideological and affective polarization). For our purpose, however, we take a broader perspective, examining and comparing across different political variables the directions—beneficial or detrimental to democracy—in which digital media effects play out.

Notwithstanding the nuances within each dimension of political behaviour, wherever possible we explicitly interpreted each change in a political variable as tending to be either beneficial or detrimental to democracy. Even though we tried to refrain from normative judgements, the nature of our research question required us to interpret the reported evidence regarding its relation to democracy. For example, an increase in political knowledge is generally considered to be beneficial under the democratic ideal of an informed citizenry 20 . Similarly, a certain level of trust in democratic institutions is crucial for a functioning democracy 33 . By contrast, various forms of polarization (particularly affective polarization) tend to split societies into opposing camps and threaten democratic decision-making 34 , 35 . Likewise, populist politics that are often coupled with right-wing nationalist ideologies, artificially divide society into a corrupt ‘elite’ that is opposed by ‘the people’, which runs counter to the ideals of a pluralistic democracy and undermines citizens’ trust in politics and the media 36 , 37 . We therefore considered polarization and populism, for example, to be detrimental to democracy.

There are already some systematic reviews of subsets of associations between political behaviour and media use that fall within the scope of our analysis, including reviews of the association between media and radicalization 38 , 39 , polarization 32 , hate speech 40 , participation 41 , 42 , 43 , 44 , 45 , echo chambers 46 and campaigning on Twitter 47 . These extant reviews, however, did not contrast and integrate the wide range of politically relevant variables into one comprehensive analysis—an objective that we pursue here. For the most relevant review articles, we matched the references provided in them with our reference list (see Materials and Methods for details). Importantly, and unlike some extant reviews, our focus is not on institutions, the political behaviour of political elites (for example, their strategic use of social media; see refs. 47 , 48 ), or higher-level outcomes (for example, policy innovation in governments 49 ). We also did not consider the effects of traditional media (for example, television or radio) or consumption behaviours that are not specific to digital media (for example, selective exposure 50 ). Furthermore, we did not focus on the microscopic psychological mechanisms that could shape polarization on social media (for a review, see ref. 51 ). For reasons of external validity, we omitted small-scale laboratory-only experiments (for example, see ref. 52 ), but included field experiments in our review. We included studies using a variety of methods—from surveys to large-scale analyses of social media data—and across different disciplines that are relevant to our research question. Details on the inclusion and exclusion criteria are provided in Materials and Methods. Our goal for this knowledge synthesis is to provide a nuanced foundation of shared facts for a constructive stage in the academic but also societal debate about the future of digital media and their role in democracy. In our view, this debate and the future design of digital media for democracy require a comprehensive assessment of its impact. We therefore not only focus on individual dimensions of political behaviour but also compare these dimensions and the methods by which they have been researched so far, thus going beyond the extant reviews. This approach aims to stimulate research that fills evidence gaps and establishes missing links that only become apparent when comparing the dimensions.

After conducting a pre-registered search (most recent update 15 September 2021) and selection process, we arrived at a final sample of N  = 496 articles. For further analysis, we classified them by the set of variables between which they report associations: type of digital media (for example, social media, online news), political variables (for example, trust, participation) and characteristics of the information ecology (for example, misinformation, selective exposure), as depicted in Fig. 1a . Each article was coded according to the combination of these variables as well as the method, specific outcome variable and, if applicable, the direction of association and potential moderator variables (see Materials and Methods for details). The resulting table of the fully coded set of studies can be found at https://osf.io/7ry4a/ , alongside the code for the analyses and visualizations offered here.

Figure 1 reports the composition of the set of included articles. Figure 1a confirms that the search query mainly returned articles concerned with the most relevant associations between digital media and political outcomes. Most of the articles were published in the last 5 years, highlighting the fast growth of interest in the link between digital media and democracy. Articles span a range of disciplines, including political science, psychology, computational science and communication science. Although a preponderance of articles focused on the United States, there was still a large geographical variation overall (see Fig. 1b ).

Figure 1c shows the distribution of measurements (counted separately when one article reported several outcomes) across methods and political variables. Our search query was designed to capture a broad range of politically relevant variables, which meant that we had to group them into broader categories. The ten most frequently reported categories of variables were trust in institutions, different variants of political participation (for example, voter turnout or protest participation), exposure to diverse viewpoints in the news, political knowledge, political expression, measures of populism (for example, support for far-right parties or anti-minority rhetoric), prevalence and spread of misinformation, measures of polarization (for example, negative attitudes towards political opponents or fragmented and adversarial discourse), homophily in social networks (that is, social connections between like-minded individuals) and online hate (that is, hate speech or hate crime). Similarly, the distribution of outcomes and associated digital media variables in Fig. 1d shows that many studies focused on political information online, and specifically political information on social media, in combination with political polarization and participation, while other digital media variables, such as messenger platforms are less explored. The full table, including the reported political variables within each category, can be found at https://osf.io/7ry4a/ . Figure 1 also reveals gaps in the literature, such as rarely explored geographical regions (for example, Africa) and under-studied methods–variable combinations (for example, involving the combination of data sources such as social media data with survey or secondary data).

Direction of associations

In the first part of our research question, we ask whether the available evidence suggests that the effects of digital media are predominantly beneficial or detrimental to democracy. To find an answer, we first selected subsets of articles that addressed the ten most frequently studied categories of political variables (hereafter simply referred to as political variables). We did not test specific hypotheses in our review. A total of N  = 354 associations were reported for these variables (when an article examined two relevant outcome variables, two associations were counted). The independent variable across these articles was always a measure of the usage of some type of digital media, such as online news consumption or social media uptake. Statistically speaking, the independent variables can be positively or negatively associated with the political outcome variable. For instance, more digital media use could be associated with more expression of hate (positive association), less expression of hate (negative association), or not associated at all. We decided to present relationships not at a statistical level but at a conceptual level. We therefore classified each observed statistical association as beneficial or detrimental depending on whether its direction was aligned or misaligned with democracy. For example, a positive statistical association between digital media use and hate speech was coded as a detrimental association; by contrast, a positive statistical association between digital media use and participation was coded as beneficial. Throughout, we represent beneficial associations in turquoise and detrimental associations in orange, irrespective of the underlying statistical polarity.

Figure 2 provides an overview of the ten most frequently studied political variables and the reported directions—colour-coded in terms of whether they are beneficial or detrimental to democracy—of each of their associations with digital media use. This overview encompasses both correlational and causal evidence. Some findings in Fig. 2 suggest that digital media can foster democratic objectives. First, the associations reported for participation point mostly in beneficial directions for democracy (aligned with previous results 45 ), including a wide range of political and civic behaviour (Fig. 1d ), from low-effort participation such as liking/sharing political messages on social media to high-cost activities such as protesting in oppressive regimes. Second, measures of political knowledge and diversity of news exposure appear to be associated with digital media in beneficial ways, but the overall picture was slightly less clear. Third, the literature is also split on how political expression is associated with digital media. Articles reporting beneficial associations between digital media and citizens’ political expression were opposed by a number of articles describing detrimental associations. These detrimental associations relate to the ‘spiral of silence’ idea, that is, the notion that people’s willingness to express their political opinions online depends on the perceived popularity of their opinions (see relevant overview articles 53 , 54 ).

Directions of associations are reported for various political variables (see Fig. 1d for a breakdown). Insets show examples of the distribution of associations with trust, news exposure, polarization and network homophily over the different digital media variables with which they were associated.

Fourth, we observed consistent detrimental associations for a number of variables. Specifically, the associations with trust in institutions were overwhelmingly pointing in directions detrimental to a functioning democracy. Measures of hate, polarization and populism were also widely reported to have detrimental associations with digital media use in the clear majority of articles. Likewise, increased digital media use was often associated with a greater exposure to misinformation. Finally, we also found that digital media were associated with homophily in social networks in detrimental ways (mostly measured on social media, and here especially on Twitter), but the pattern of evidence was a little less consistent. Differences in the consistency of results were also reflected when broken down along associated digital media variables (see insets in Fig. 2 ). For instance, both trust and polarization measures were consistently associated with media use across types of digital media ranging from social media to political information online; in contrast, results for homophily were concentrated on social media and especially on Twitter, while measurements of news exposure were mostly concentrated on political information online.This points not only to different operationalizations of related outcome measures, such as diverse information exposure and homophilic network structures, but also to differences between the distinct domains of digital media in which these very related phenomena are measured. Similar observations can be made when separating associations between general types of digital media: social media vs internet more broadly (Supplementary Fig. 1 ).

Next, we distinguished between articles reporting correlational versus causal evidence and focused on the small subset of articles reporting the latter ( N  = 24). We excluded causal evidence on the effects of voting advice applications from our summary as a very specific form of digital media, explicitly constructed to inform vote choices, and already extensively discussed in a meta-analysis 55 .

Causal inference

Usually, the absence of randomized treatment assignment, an inescapable feature of observational data (for example, survey data), precludes the identification of causal effects because individuals differ systematically on variables other than the treatment (or independent) variable. However, under certain conditions, it is possible to rule out non-causal explanations for associations, even in studies without random assignment that are based on observational data (see refs. 56 , 57 , 58 ). For a more detailed explanation of the fundamental principles of causal inference, see Supplementary Material page 5 and, for example, the work of the 2021 laureates of the Nobel Memorial Prize in Economics 56 , 57 , 58 .

Common causal inference techniques that were used in our sample include instrumental variable designs that introduce exogenous variation in the treatment variable 59 , 60 , 61 , 62 , 63 , matching approaches to explicitly balance treatment and control groups 64 , 65 , 66 , and panel designs that account for unobserved confounders with unit and/or time-fixed effects 67 , 68 . We also found multiple large-scale field experiments conducted on social media platforms 69 , 70 , 71 , 72 as well as various natural experiments 59 , 61 , 62 , 73 .

Figure 3 summarizes the findings and primary causal inference techniques of these articles. Again, causal effects were coded as beneficial for or detrimental to democracy. This figure is structured according to whether evidence stemmed from established democracies or from emerging democracies and authoritarian regimes, adopting classifications from the Liberal Democracy Index provided by the Varieties of Democracy project 18 . In some autocratic regimes (for example, China), it is particularly difficult to interpret certain effects. For example, a loss of trust in government suggests a precarious development for an established democracy; in authoritarian regimes, however, it may indicate a necessary step toward overcoming an oppressive regime and, eventually, progressing towards a more liberal and democratic system. Instead of simply adopting the authors’ interpretation of the effects or imposing our own interpretation of effects in authoritarian contexts, we leave this interpretation to the reader (denoted in purple in the figure). The overall picture converges closely with the one drawn in Fig. 2 . We found general trends of digital media use increasing participation and knowledge but also increasing political polarization and decreasing trust that mostly aligned with correlational evidence.

Each box represents one article. Treatments (T) are in white boxes on the left, political outcome (O) variables in coloured boxes on the right; M denotes mediators; H represents sources of effect heterogeneity or moderators. Positive (+) and negative (−) signs at paths indicate reported direction of effects. Location of sample indicated in top right corner of boxes, primary causal inference strategy in bottom left. Strategies include statistical estimation strategies such as instrumental variables (IV), matching and panel designs (PD) that use, for example, fixed effects (FE) or difference in difference (DiD) for causal estimation, as well as lab or field experiments (for example, field experiments rolled out on various platforms that are often supplemented with IV estimation to account for imperfect compliance). Detrimental effects on liberal democracy are shown in orange, beneficial effects in turquoise, effects open to interpretation in purple and null effects in grey. Solid arrows represent pathways for which authors provide causal identification strategies, dashed arrows represent descriptive (mediation) pathways.

Effects on key political variables

In the following sections, we provide a short synopsis of the results, point to conflicting trends and highlight some examples of the full set of correlational and causal evidence, reported in Figs. 2 and 3 , for six variables that we found to be particularly crucial for democracy: participation, trust, political knowledge, polarization, populism, network structures and news exposure. The chosen examples are stand-ins and illustrations of the general trends.

Participation

Consistent with past meta-analyses 42 , 43 , 45 , the body of correlational evidence supported a beneficial association between digital media use and political participation and mobilization.

Causal analyses of the effects of digital media on political participation in established democracies mostly studied voting and voter turnout 64 , 67 , 71 , 74 , 75 , 76 ; articles concerned with other regions of the world rather focused on political protest behaviour 59 , 61 , 66 . Other articles considered online political participation 65 , 71 . One study, applying causal mediation analysis to assess a causal mechanism 77 , found that information-oriented social media use affects political participation, mediated or enabled through the user’s online political efficacy 65 . Overall, our evidence synthesis found largely beneficial mobilizing effects for political participation across this set of articles. Our search did not identify any studies that examined causal effects of digital media on political participation in authoritarian regimes in Africa or the Middle East.

Many articles in our sample found detrimental associations between digital media and various dimensions of trust (Fig. 2 ). For example, detrimental associations were found for trust in governments and politics 59 , 60 , 66 , 78 , 79 , 80 , 81 , 82 , trust in media 83 , and social and institutional trust 84 . During the COVID-19 pandemic, digital media use was reported to be negatively associated with trust in vaccines 85 , 86 . Yet the results about associations with trust are not entirely homogeneous. One multinational survey found beneficial associations with trust in science 87 ; others found increasing trust in democracy with digital media use in Eastern and Central European samples 88 , 89 . Nevertheless, the large majority of reported associations between digital media use and trust appear to be detrimental for democracy. While the evidence stems mostly from surveys, results gathered with other methods underpin these findings (Fig. 2 inset).

The majority of articles identifying causal effects also find predominantly detrimental effects of digital media on trust. A field experiment in the United States that set browser defaults to partisan media outlets 37 found a long-term loss of trust in mainstream media. Studies examining social trust as a central component of social capital find consistent detrimental effects of social media use 84 ; in contrast, no effects of broadband internet in general on social trust was found 90 . In authoritarian regimes in Asia, increasing unrestricted internet access decreased levels of trust in the political system 59 , 73 , 91 . This finding confirms the predominant association observed in most other countries. Yet it also illustrates how digital media is a double-edged sword, depending on the political context: by reducing trust in institutions, digital media can threaten existing democracies as well as foster emerging democratic developments in authoritarian regimes.

Political knowledge

The picture was less clear for associations between the consumption of digital media and political knowledge. Still, the majority of associations point in beneficial directions and were found in both cross-sectional surveys 92 , 93 , 94 , 95 , 96 , 97 , 98 , 99 and panel surveys 100 , 101 , 102 . Studies linking web-tracking and survey data showed increased learning about politics 103 , but also a turning away from important topics 104 , whereas other experiments demonstrated an overall beneficial effect of digital media on issue salience 105 . These findings, however, stand in contrast to other studies that find a detrimental association between political knowledge and digital media use 106 , 107 , 108 , 109 , 110 .

The body of causal evidence on political knowledge also tends to paint a relatively promising picture. Multiple articles found that engagement with digital media increased political knowledge 67 , 70 , 72 , 74 and that engagement with political content on social media increased political interest among adolescents 111 . In line with these findings, it has been reported that political messages on social media, as well as faster download speed, can increase information-seeking in the political domain 67 , 71 . By contrast, there is evidence for a decrease in political knowledge 112 , which is mediated through the news-finds-me effect: social media users believe that actively seeking out news is no longer required to stay informed, as they expect to be presented with important information.

It is important to note that most of these effects are accompanied by considerable heterogeneity in the population that benefits and the type of digital media. For example, politically interested individuals showed higher knowledge acquisition when engaging with Twitter, whereas the opposite effects emerged for engagement with Facebook 113 . Furthermore, there is evidence that the news-finds-me effect on social media can be mitigated when users consult alternative news sources 112 .

Polarization

Most articles found detrimental associations between digital media and different forms of political polarization 114 , 115 , 116 , 117 , 118 . Our review obtained evidence for increasing outgroup polarization on social media in a range of political contexts and on various platforms 119 , 120 , 121 , 122 . Increasing polarization was also linked to exposure to viewpoints opposed to one’s own on social media feeds 69 , 123 . Articles comparing several political systems found associations that were country-dependent 124 , again highlighting the importance of political context 125 . Nevertheless, high digital media use was for the most part linked to higher levels of polarization, although there was some evidence for balanced online discourse without pronounced patterns of polarization 126 , 127 , 128 , as well as evidence for potentially depolarizing tendencies 129 .

The body of causal articles largely supported the detrimental associations of digital media that emerged, by and large, in the correlational articles. Among established democracies, both social media use and overall internet use increased political polarization 63 , 70 . This was also the case for an experimental treatment that exposed users to opposing views on Twitter 69 . However, some findings run counter to the latter result 130 : in a 2 month field experiment, exposure to counter-attitudinal news on Facebook reduced affective polarization (the authors used opposing news outlets as treatment instead of opinions on social media). Furthermore, one other field experiment did not find evidence that exposure to partisan online news substantively shifted political opinions but found a long-term loss of trust in mainstream media 37 . Still, taking all evidence into account, the overall picture remains largely consistent on the detrimental association between digital media and political polarization, including some but not all causal evidence.

Articles on populism in our review examined either vote share and other popularity indicators for populist parties or the prevalence of populist messages and communication styles on digital media. Overall, articles using panel surveys, tracking data and methods linking surveys to social media data consistently found that digital media use was associated with higher levels of populism. For example, digital platforms were observed to benefit populist parties more than they benefit established politicians 131 , 132 , 133 , 134 . In a panel survey in Germany, a decline in trust that accompanied increasing digital media consumption was also linked to a turn towards the hard-right populist AfD party 80 . This relationship might be connected to AfD’s greater online presence, relative to other German political parties 132 , even though these activities might be partly driven by automated accounts. There is also evidence for an association between increased social media use and online right-wing radicalization in Austria, Sweden and Australia 135 , 136 , 137 . Only a minority of articles found no relationship or the reverse relationship between digital media and populism 138 , 139 , 140 . For instance, in Japan, internet exposure was associated with increased tolerance towards foreigners 141 .

Similarly, most causal inference studies linked increased populism to digital media use. For instance, digital media use in Europe led to increased far-right populist support 63 , 142 , and there was causal evidence that digital media can propagate ethnic hate crimes in both democratic and authoritarian countries 62 , 68 . Leaving the US and European political context, in Malaysia, internet exposure was found to cause decreasing support for the authoritarian, populist government 60 .

Echo chambers and news exposure

The evidence on echo chambers points in different directions depending on the outcome measure. On the one hand, when looking at news consumption, several articles showed that social media and search engines diversify people’s news diets 67 , 143 , 144 , 145 , 146 . On the other hand, when considering social networks and the impact of digital media on homophilic structures, the literature contains consistent reports of ideologically homogeneous social clusters 147 , 148 , 149 , 150 , 151 . This underscores an important point: some seemingly paradoxical results can potentially be resolved by looking more closely at context and specific outcome measurement (see also Supplementary Fig. 2 ). The former observation of diverse news exposure might fit with the beneficial relationship between digital media and knowledge reported in refs. 67 , 74 , 94 , 95 , 102 , and the homophilic social structures could be connected to the prevalence of hate speech and anti-outgroup sentiments 120 , 152 , 153 , 154 , 155 .

Heterogeneity

We now turn to the second part of our research question and analyse the effects of digital media use in light of different political contexts. Figure 4 shows the geographical distribution of effect directions around the globe. Notably, most beneficial effects on democracy were found in emerging democracies in South America, Africa and South Asia. Mixed effects, by contrast, were distributed across Europe, the United States, Russia and China. Similarly, detrimental outcomes were mainly found in Europe, the United States and partly Russia, although this may reflect a lack of studies undertaken in authoritarian contexts. These patterns are also shown in Fig. 4c,d , where countries are listed according to the Liberal Democracy Index. Moderators—variables such as partisanship and news consumption that are sources of effect heterogeneity—displayed in Supplementary Fig. 3 also show slight differences between outcomes. Beneficial outcomes seemed to be more often moderated by political interest and news consumption, whereas detrimental outcomes tended to be moderated by political position and partisanship.

a , Geographical distribution of reported associations for the variables trust, knowledge, participation, exposure and expression. Pie charts show the composition of directions for each country studied. b , Geographic representation of reported associations for the variables hate, polarization, populism, homophily and misinformation. c , Data and variables in a , in absolute numbers of reported associations and sorted along the Liberal Democracy Index 18 . d , Data and variables in b , in absolute numbers of reported associations and sorted along the Liberal Democracy Index.

Furthermore, many causal articles acknowledge that effects differ between subgroups of their sample when including interaction terms in their statistical models. For example, the polarizing effects of digital media differ between Northern and Southern European media systems 142 : while consumption of right-leaning digital media increased far-right votes, especially in Southern Europe, the consumption of news media and public broadcasting in Northern European media systems with high journalistic standards appears to mitigate these effects. Another example of differential effects between subgroups was found in Russia, where the effects of social media on xenophobic violence were only present in areas with pre-existing nationalist sentiment. This effect was especially pronounced for hate crimes with a larger number of perpetrators, indicating that digital media was serving a coordinating function. In summary, a range of articles found heterogeneity in effects for varying levels of political interest 67 , 113 , political orientation 63 , 69 , 70 and different characteristics of online content 111 .

Most authors, particularly those of the causal inference articles in our body of evidence, explicitly emphasized the national, cultural, temporal and political boundary conditions for interpreting and generalizing their results (see, for example, ref. 111 ). By contrast, especially in articles conducted on US samples, the national context and the results’ potential conditionality was often not highlighted. We strongly caution against a generalization of findings that are necessarily bound to a specific political setting (for example, the United States) to other contexts.

Sampling methods and risk of bias

To assess study quality and risk of bias, we additionally coded important methodological aspects of the studies, specifically, the sampling method, sample size and transparency indicators, such as competing interest, open data practices and pre-registrations. In Fig. 5 , we show an excerpt from that analysis. Different sampling methods naturally result in different sample sizes as shown in Fig. 5a,b . Furthermore, behavioural data are much more prevalent for studies that look at detrimental outcomes, such as polarization and echo chambers. Classic surveys with probability samples or quota samples, in contrast, are often used to examine beneficial outcome measures such as trust and participation (Fig. 5c,d ). Overall, however, no coherent pattern emerges in terms of the reported directions of associations. If anything, large probabilistic samples report relatively less beneficial associations for both types of outcomes (Fig. 5 ). Generally, different types of data have different advantages, such as probability and quota samples approximating more closely the ideal of representativeness, whereas the observation of actual behaviour on social media escaping the potential downsides of self-reporting. A potential blind spot in studies working with behavioural data from social media, inaccessible to both us and the original authors of the studies, is the selection of data provided by platforms. Therefore, it is tremendously important for researchers to get unrestricted access or, at least, transparent provision of random samples of data by platforms. The selection of users into the platforms, however, remains an open issue for behavioural data as it is often unclear who the active users are and why they are active online. We find that political outcome measures studied with behavioural data appear to show quite distinct results compared with those studied with large-scale survey data. Combining both data types would probably maximize the chances for reliable conclusions about the impact of digital media on democracy.

a , Sample size vs sampling methods for variables of trust, knowledge, participation, exposure and expression. Each dot represents one measurement, colour coded according to the direction of the reported association. b , Sample size vs sampling method for variables of hate, polarization, populims, network homophily and misinformation. c , More detailed breakdown for the same varibales as in a of sampling methods and their respective counts of reported associations and their direction. d , Breakdown of sampling methods and counts of associations for the same variables as in b .

We found relatively few null effects for some variables. This could be accurate, but it could also be driven by the file-drawer problem—the failure to publish null results. To examine the extent of a potential file-drawer problem, we contacted authors via large mailing lists but did not receive any unpublished work that fitted our study selection criteria. Regarding possible risk of bias, we found that only in 143 out of 354 measurements did authors clearly communicate that no conflict of interest was present (beyond the usual funding statement). However, we did not find a striking imbalance in the distribution of reported associations between those articles that did not explicitly state competing interest and those that did. Of the few associations for which conflicts of interest were stated, 4 pointed in beneficial, 3 in detrimental and 2 reported lack of directionality. In only 79 of 354 measurements did the researchers use open data practices. Considering articles that reported detrimental associations, we did not find a clear difference in the directions between those with and without open data. However, considering articles that reported beneficial outcomes, the numbers of positive findings in the studies without open data are relatively much larger than for the open science studies. Namely, 103 beneficial and 33 detrimental associations were reported in those without open data, while 19 beneficial versus 14 detrimental were reported in studies with open data practices. This observation might be due to the large number of survey-based studies about participation, which often do not follow open data practices. Even fewer of the studies in our sample were pre-registerd, namely, 13 of the 354, where 9 reported detrimental associations, only 3 reported beneficial associations and 1 found no direction of association. To shed light on other potential biases, we additionally examined temporal variations in the directions of reported associations and found, besides the general explosive growth of studies in this domain, a slight trend towards an increasing number of both detrimental directions and null effects over time (Supplementary Fig. 4 ). At the author level, there was no clear pattern in the associations reported by those authors who published the greatest number of articles in our sample; several authors variously reported detrimental and beneficial effects as well as null effects, with a few exceptions (Supplementary Fig. 5 ). Their co-authorship network in Supplementary Fig. 6 , split for the two types of outcomes measures, shows some communities of co-authors; however, no clear pattern of preferred direction of reported association can be spotted. Overall, we did not find evidence of a systematic bias in either direction driven by temporal trends or particular authors.

Regardless of whether they are authoritarian, illiberal, or democratic, governments around the world are concerned with how digital media affect governance and their citizenry’s political beliefs and behaviours. A flurry of recent interdisciplinary research, stimulated in part by new methodological possibilities and data sources, has shed light on this potential interplay.

Although classical survey methods are still predominant, novel ways of linking data types, for example linking URL tracking data or social media data with surveys, permit more complex empirical designs and analyses. Furthermore, digital trace data allow an expansion in sample size. The articles we reviewed included surveys with a few hundred, up to a few thousand participants, but also large-scale social media analyses that included behavioural traces of millions. Yet with computational social science still in its early days, the amount of evidence supporting and justifying causal conclusions is still limited. Causal effects of digital media on political variables are also hard to pin down empirically due to a plethora of complexities and context factors, as well as the highly dynamic technological developments that make predicting the future difficult. While emergent political phenomena are hard to simulate in the lab, the value of estimation and data collection strategies to draw causal inferences from real-life data is enormous. However, the long-established trade-off between internal and external validity still applies, which also highlights the value of high-quality descriptive work.

Taking into account both correlational and causal evidence, our review suggests that digital media use is clearly associated with variables such as trust, participation and polarization. They are critical for the functioning of any political system, in particular democracies. Extant research reports relatively few null effects. However, the trends on each factor mostly converge, both across research methods and across correlative and causal evidence.

Our results also highlight that digital media are a double-edged sword, with both beneficial and detrimental effects on democracy. What is considered beneficial or detrimental will, at least partly, hinge on the political system in question: intensifying populism and network homophily may benefit a populist regime or a populist politician but undermine a pluralistic democracy. For democratic countries, evidence clearly indicates that digital media increase political participation. Less clear but still suggestive are the findings that digital media have positive effects on political knowledge and exposure to diverse viewpoints in news. On the negative side, however, digital media use is associated with eroding the ‘glue that keeps democracies together’ 33 : trust in political institutions. The results indicating this danger converge across methods. Furthermore, our results also suggest that digital media use is associated with increases in hate, populism and polarization. Again, the findings converge across causal and correlational articles.

Alongside the need for more causal evidence, we found several research gaps, including the relationship between trust and digital media and the seeming contradiction between network homophily and diverse news exposure. Methods that link tracking data for measuring news exposure with behavioural data from social media (for example, sharing activities or the sentiment of commenting) are crucial to a better understanding of this apparent contradiction.

Limitations

The articles in our sample incorporate a plethora of methods and measures. As a result, it was necessary to classify variables and effects into broad categories. This is a trade-off we had to make in exchange for the breadth of our overview of the landscape of evidence across disciplines. For the same reason, we could not provide a quantitative comparison across the diverse sample of articles. We believe that digital media research would benefit from more unified measures (for example, for polarization), methods across disciplines to allow for better comparability in the future, a systematic comparison of different types of digital media (that is, Facebook and Twitter are neither of one kind nor, in all likelihood, are their effects) and extensions of outcome measurements beyond certain types of digital media. This follows other recent calls for commensurate measures of political and affective polarization 156 . The breadth of our review and the large number of political outcome measures in particular, made it necessary to be quite restrictive on other ends (see Fig. 6 for our exclusion process and Supplementary Table 1 for the detailed criteria). We explicitly decided to prioritize the selection of causal evidence (see Fig. 7 for an overview of the causal inference techniques that we considered) and other large-sample, quantitative, published evidence. However, following this pre-registered search strategy led to the selection of unequal numbers of studies for different outcome variables. For example, our search query selected considerably more studies examining political participation than political expression or trust, while at the same time, it did not include all studies that are included in other systematic reviews 45 due to stricter exclusion criteria.

a , Keywords included in our search query, run on Web of Science and Scopus, with logical connectors. Focus was on causal inference methods (method column), but also inclusion of descriptive quantitative evidence, relationships between digital media (cause column) and political outcomes (direct effect box) or content features (indirect effect box). b , Flowchart representing the stepwise exclusion process, starting with title-based exclusion, followed by abstract-based exclusion. c , Example illustration of outcome variable extraction from the abstracts. d , Breakdown of the most frequently reported political variables into top 10 categories. Numbers in brackets are counts of measurements in the set.

Fundamental principles of causal inference techniques and statistical strategies used in our sample of causal evidence (excluding field experiments).

The interpretation of our results was in several cases hampered by a lack of appropriate baseline measures. There is no clear measure of what constitutes a reasonable benchmark of desirable political behaviour in a healthy democracy. In addition, there were no means of quantification of some of these behaviours in the past, outside of digital media. This problem is particularly pronounced for factors such as exposure to diverse news, social network homophily, misinformation and hate speech. Measuring these phenomena at scale is possible through digital media (for example, by analysing social network structure); much less is known about their prevalence and dynamics in offline settings. Many articles therefore lacked a baseline. For instance, it is neither clear what level of homophily in social networks is desirable or undesirable in a democratic society, nor is it clear how to interpret the results of certain studies on polarization 69 , 130 , whose findings depend on whether one assumes that social media have increased or decreased exposure to opposing views relative to some offline benchmark. For example, if exposure to opposing views is increased on social media, the conclusion of one study 130 would be that it reduces polarization, but if exposure is decreased, one would come to the opposite conclusion. Notably, in this study, counter-attitudinal exposure was found to be down-ranked by Facebook’s news feed—hence supporting a process that fosters polarization instead of counteracting it. Furthermore, results about populism might be skewed: descriptive evidence on the relative activity and popularity of right-wing populist parties in Europe suggests their over-representation, as in the case of Germany’s AfD, on social media, relative to established democratic parties (see, for example, ref. 132 ). Therefore, it is difficult to interpret even causal effects of digital media use on populist support in isolation from the relative preponderance of right-wing content online.

Our results provide grounds for concern. Alongside the positive effects of digital media for democracy, there is clear evidence of serious threats to democracy. Considering the importance of these corrosive and potentially difficult-to-reverse effects for democracy, a better understanding of the diverging effects of digital media in different political contexts (for example, authoritarian vs democratic) is urgently needed. To this end, methodological innovation is required. This includes, for instance, more research using causal inference methodologies, as well as research that examines digital media use across multiple and interdependent measures of political behaviour. More research and better study designs will, however, also depend on access to data collected by the platforms. This access has been restricted or foreclosed. Yet without independent research that has unhampered access to all relevant data, the effects of digital media can hardly be understood in time. This is even more concerning because digital media can implement architectural changes that, even if seemingly small, can scale up to widespread behavioural effects. Regulation may be required to facilitate this access 157 . Most importantly, we suggest that the bulk of empirical findings summarized here can be attributed to the current status quo of an information ecosystem produced and curated by large, commercial platforms. They have succeeded in attracting a vast global audience of users. The sheer size of their audience as well as their power over what content and how content gets the most attention has led, in the words of the philosopher Jürgen Habermas, to a new structural transformation of the public sphere 16 . In this new public sphere, everybody can be a potential author spontaneously producing content, both right-wing radical networks as well as the courageous Belarusian women standing up for human rights and against a repressive regime. One need not share Habermas’ conception of ‘deliberate democracy’ to see that current platforms fail to produce an information ecosystem that empowers citizens to make political choices that are as rationally motivated as possible. Our results show how this ecosystem plays out to have important consequences for political behaviours and attitudes. They further underscore that finding out which aspects of this relationship are detrimental to democracy and how they can be contained while actively preserving and fostering the emancipatory potential of digital media is, perhaps, one of the most important global tasks of the present. Our analysis hopes to contribute to the empirical basis of this endeavour.

This systematic review follows the MOOSE Guidelines for Meta-Analyses and Systematic Reviews of Observational Studies 158 . The detailed protocol of the review process was pre-registered on the Open Science Framework (OSF) at https://osf.io/7ry4a/ . The repository also contains the completed MOOSE checklist showing where each guideline is addressed in the text.

Figure 6 summarizes the search query that we used on two established academic databases, Scopus and Web of Science (both highly recommended search tools), the resulting number of articles from the query and the subsequent exclusion steps, leading to the final sample size of N  = 496 articles under consideration 159 , 160 , 161 , 162 , 163 , 164 , 165 , 166 , 167 , 168 , 169 , 170 , 171 , 172 , 173 , 174 , 175 , 176 , 177 , 178 , 179 , 180 , 181 , 182 , 183 , 184 , 185 , 186 , 187 , 188 , 189 , 190 , 191 , 192 , 193 , 194 , 195 , 196 , 197 , 198 , 199 , 200 , 201 , 202 , 203 , 204 , 205 , 206 , 207 , 208 , 209 , 210 , 211 , 212 , 213 , 214 , 215 , 216 , 217 , 218 , 219 , 220 , 221 , 222 , 223 , 224 , 225 , 226 , 227 , 228 , 229 , 230 , 231 , 232 , 233 , 234 , 235 , 236 , 237 , 238 , 239 , 240 , 241 , 242 , 243 , 244 , 245 , 246 , 247 , 248 , 249 , 250 , 251 , 252 , 253 , 254 , 255 , 256 , 257 , 258 , 259 , 260 , 261 , 262 , 263 , 264 , 265 , 266 , 267 , 268 , 269 , 270 , 271 , 272 , 273 , 274 , 275 , 276 , 277 , 278 , 279 , 280 , 281 , 282 , 283 , 284 , 285 , 286 , 287 , 288 , 289 , 290 , 291 , 292 , 293 , 294 , 295 , 296 , 297 , 298 , 299 , 300 , 301 , 302 , 303 , 304 , 305 , 306 , 307 , 308 , 309 , 310 , 311 , 312 , 313 , 314 , 315 , 316 , 317 , 318 , 319 , 320 , 321 , 322 , 323 , 324 , 325 , 326 , 327 , 328 , 329 , 330 , 331 , 332 , 333 , 334 , 335 , 336 , 337 , 338 , 339 , 340 , 341 , 342 , 343 , 344 , 345 , 346 , 347 , 348 , 349 , 350 , 351 , 352 , 353 , 354 , 355 , 356 , 357 , 358 , 359 , 360 , 361 , 362 , 363 , 364 , 365 , 366 , 367 , 368 , 369 , 370 , 371 , 372 , 373 , 374 , 375 , 376 , 377 , 378 , 379 , 380 , 381 , 382 , 383 , 384 , 385 , 386 , 387 , 388 , 389 , 390 , 391 , 392 , 393 , 394 , 395 , 396 , 397 , 398 , 399 , 400 , 401 , 402 , 403 , 404 , 405 , 406 , 407 , 408 , 409 , 410 , 411 , 412 , 413 , 414 , 415 , 416 , 417 , 418 , 419 , 420 , 421 , 422 , 423 , 424 , 425 , 426 , 427 , 428 , 429 , 430 , 431 , 432 , 433 , 434 , 435 , 436 , 437 , 438 , 439 , 440 , 441 , 442 , 443 , 444 , 445 , 446 , 447 , 448 , 449 , 450 , 451 , 452 , 453 , 454 , 455 , 456 , 457 , 458 , 459 , 460 , 461 , 462 , 463 , 464 , 465 , 466 , 467 , 468 , 469 , 470 , 471 , 472 , 473 , 474 , 475 , 476 , 477 , 478 , 479 , 480 , 481 , 482 , 483 , 484 , 485 , 486 , 487 , 488 , 489 , 490 , 491 , 492 , 493 , 494 , 495 , 496 , 497 , 498 , 499 , 500 , 501 , 502 , 503 , 504 , 505 , 506 , 507 , 508 , 509 , 510 , 511 , 512 , 513 , 514 , 515 , 516 , 517 , 518 , 519 , 520 , 521 , 522 , 523 , 524 , 525 , 526 , 527 , 528 , 529 , 530 , 531 , 532 , 533 , 534 , 535 , 536 , 537 , 538 , 539 , 540 , 541 , 542 , 543 , 544 , 545 , 546 , 547 , 548 , 549 , 550 , 551 , 552 , 553 , 554 , 555 , 556 , 557 , 558 , 559 , 560 , 561 , 562 , 563 , 564 , 565 , 566 , 567 , 568 , 569 , 570 , 571 , 572 , 573 , 574 , 575 .

Study selection criteria

We included only original, empirical work. Conceptual or theoretical work, simulation studies and evidence synthesizing studies were excluded. Articles had to be published in academic journals in English. Unpublished studies for which only the abstract or a preprinted version was available were excluded from the review. We excluded small- N laboratory experiments and small- N student surveys ( N  < 100) from our body of original work due to validity concerns. Although correlational evidence cannot establish a causal direction, we focused on articles that examined effects of digital media on democracy but not the opposite. We therefore excluded, for example, articles that examined ways to digitize democratic procedures. To be included, articles had to include at least two distinct variables, a digital media variable and a political outcome. Articles measuring a single variable were only included if this variable was a feature of digital media (for example, hate speech prevalence, homophily in online social networks, prevalence of misinformation in digital media).

Search strategy, study selection, coding and data extraction

Articles eligible for our study had to be published before 15 September 2021. We sourced our review database from Scopus and Web of Science, as suggested by ref. 159 . The search query (Fig. 6 ) was constructed in consultation with professional librarians and was designed to be as broad as possible to pick up any articles containing original empirical evidence of direct or indirect effects of digital media on democracy (including correlational evidence). We further consulted recent, existing review articles in the field 32 , 39 , 40 to check for important articles that did not appear in the review body. Articles that were included manually are referenced separately in the flowchart (Fig. 6 ). In addition, we contacted authors via large mailing lists of researchers working on computational social science and misinformation but did not receive any unpublished work that fitted our study selection criteria. The query retrieved N  = 3,509 articles. Of these, 1,349 were retained after screening the titles for irrelevant topics. This first coding round, whether an article, based on the title, fits the review frame or not, was split between two coders. Coders could flag articles that are subject to discussion to let the other coder double check the decision. In this round, only clearly not fitting articles were excluded from the sample. A list of exclusion criteria can be found in SuppIementary Information .

The next coding round, whether an article, based on the abstract, fits the review frame, was conducted in parallel by two coders. The inter-coder reliability, after this round of article selection, was Krippendorff’s alpha of 0.66 (87% agreement). After calculating this value, disagreement between coders was solved through discussion. At this stage, we excluded all studies that were not original empirical work, such as other reviews or conceptual articles, simulation studies and purely methodological articles (for example, hate speech or misinformation detection approaches). This coding round was followed by a more in-depth coding round. Here we refined our exclusion decisions; for example, we excluded studies that examined the digitization of government, preprints, small-scale lab experiments, small-scale convenience or student samples and studies that only included one variable (for example, description of online forums) (see Supplementary Table 1 for a detailed list of criteria). A full-text screen was performed in cases where the relevant information could not be retrieved from the abstract and for all articles implying causal evidence.

After both rounds of abstract screening, 474 articles remained in our sample. After cross-checking the results of our literature search against the references from existing reviews, we found and included further N  = 22 articles that met our thematic criteria but were not identified by our search string. Ultimately, a total of 496 articles were selected into the final review sample. Figure 6b summarizes the selection procedure.

The following information was extracted from each article using a standardized data extraction form: variable groups under research (digital media, features of media and/or political outcome variables), the concrete digital media under research, the explicit political outcome variable, the methods used, the country of origin, causal claims, possible effect heterogeneity (moderation) as well as various potential sources of bias. To assess various quality criteria of the studies, the coders had to visit the full text of the articles (for example, to find the declaration of competing interests, pre-registration or data availability statements, or to consider the methods section). Therefore, and facing the large number of articles under consideration, blinding could not be established during this procedure.

When conducting a systematic review with a broad scope, categories of the variables cannot be exhaustively defined before coding. Therefore, variable categories, especially for the digital media variables and the political outcome variables, were chosen inductively. In the first extraction step, coders stuck closely to the phrasing of the authors of the respective study. To reduce redundancy and refine the clustering of the variables, we iteratively generated frequency tables and manually sorted single variables to the best-fitting categories until a small number of clearly distinct categories was selected. After the categories were defined, both coders re-coded 10% of the sample to calculate inter-coder reliabilities for all key variables. We provide a table of inter-coder reliabilities (percentage agreements and Krippendorff’s alphas) (Supplementary Table 2 ).

Data synthesis and analysis

Due to considerable heterogeneity in methods in the articles—including self-report surveys through network analysis of social media data, URL tracking data and field experiments—no calculation of meta-analytic effect sizes was possible. The final table of selected articles with coded variables will be published alongside this article as a major result of this review project. The effect directions of 10 important political outcome variables (4 consistent with liberal democracy, 4 opposing democratic values) are summarized in Fig. 2 . For articles dealing with these political variables, we also assessed the country in which the study was conducted (Fig. 4 ), as well as explicit sources of effect heterogeneity such as demographic characteristics of study participants or characteristics of the digital media platform.

For the overview analysis, which includes both correlational and causal evidence, we mainly restricted ourselves to the evaluation effects reported in the abstracts. Articles making explicit causal claims and/or using causal inference methods (Fig. 7 ) were examined in-depth and summarized as simplified path diagrams with information on mediators, moderators, country of origin and method used (Fig. 3 ).

Deviations from the protocol

The volume of papers our query returned prevented an in-depth analysis of confounding variables. Instead, our assessment of quality relied on the sampling strategy and sample size, the method used, sources of heterogeneity and transparency criteria, such as open data practices and pre-registration. Furthermore, we were able to construct the co-author network by matching the author’s names, but were unable to produce a meaningful co-citation network due to the incompleteness and ambiguity of references in the export format that we used.

Reporting summary

Further information on research design is available in the Nature Research Reporting Summary linked to this article.

Data availability

The dataset including all originally collected studies with decision stages ( N  = 3,531, ‘full_data.xlsx’), the table including all papers within our review sample ( N  = 496, ‘data_review.xlsx’) and the table including all effects reported within papers dealing with the top ten outcome measures ( N  = 354, ‘data_effects.xlsx’) are available at https://osf.io/7ry4a/ .

Code availability

R scripts for all analyses and figures are available at https://osf.io/7ry4a/ .

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Acknowledgements

We thank S. Munzert for providing his perspective on causal inference and issues specific to political science, D. Ain for editing the manuscript and F. Stock for help in the literature comparison. P.L.-S., S.L. and R.H. acknowledge financial support from the Volkswagen Foundation (grant ‘Reclaiming individual autonomy and democratic discourse online: How to rebalance human and algorithmic decision-making’). S.L. acknowledges support from the Humboldt Foundation through a research award and partial support by an ERC Advanced Grant (PRODEMINFO) during completion of this paper. L.O. acknowledges financial support by the German National Academic Foundation in the form of a PhD scholarship. The authors received no specific funding for this work. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.

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Lorenz-Spreen, P., Oswald, L., Lewandowsky, S. et al. A systematic review of worldwide causal and correlational evidence on digital media and democracy. Nat Hum Behav 7 , 74–101 (2023). https://doi.org/10.1038/s41562-022-01460-1

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This Essay is a critical reflection on the impact of the digital revolution and the internet on three topics that shape the contemporary world: democracy, social media, and freedom of expression. Part I establishes historical and conceptual assumptions about constitutional democracy and discusses the role of digital platforms in the current moment of democratic recession. Part II discusses how, while social media platforms have revolutionized interpersonal and social communication and democratized access to knowledge and information, they also have led to an exponential spread of mis- and disinformation, hate speech, and conspiracy theories. Part III proposes a framework that balances regulation of digital platforms with the countervailing fundamental right to freedom of expression, a right that is essential for human dignity, the search for the possible truth, and democracy. Part IV highlights the role of society and the importance of media education in the creation of a free, but positive and constructive, environment on the internet.

I. Introduction

Before the internet, few actors could afford to participate in public debate due to the barriers that limited access to its enabling infrastructure, such as television channels and radio frequencies. 1 Digital platforms tore down this gate by creating open online communities for user-generated content, published without editorial control and at no cost. This exponentially increased participation in public discourse and the amount of information available. 2 At the same time, it led to an increase in disinformation campaigns, hate speech, slander, lies, and conspiracy theories used to advance antidemocratic goals. Platforms’ attempts to moderate speech at scale while maximizing engagement and profits have led to an increasingly prominent role for content moderation algorithms that shape who can participate and be heard in online public discourse. These systems play an essential role in the exercise of freedom of expression and in democratic competence and participation in the 21st century.

In this context, this Essay is a critical reflection on the impacts of the digital revolution and of the internet on democracy and freedom of expression. Part I establishes historical and conceptual assumptions about constitutional democracy; it also discusses the role of digital platforms in the current moment of democratic recession. Part II discusses how social media platforms are revolutionizing interpersonal and social communication, and democratizing access to knowledge and information, but also lead to an exponential spread of mis- and disinformation, hate speech and conspiracy theories. Part III proposes a framework for the regulation of digital platforms that seeks to find the right balance with the countervailing fundamental right to freedom of expression. Part IV highlights the role of society and the importance of media education in the creation of a free, but positive and constructive, environment on the internet.

II. Democracy and Authoritarian Populism

Constitutional democracy emerged as the predominant ideology of the 20th century, rising above the alternative projects of communism, fascism, Nazism, military regimes, and religious fundamentalism . 3 Democratic constitutionalism centers around two major ideas that merged at the end of the 20th century: constitutionalism , heir of the liberal revolutions in England, America, and France, expressing the ideas of limited power, rule of law, and respect for fundamental rights; 4 and democracy , a regime of popular sovereignty, free and fair elections, and majority rule. 5 In most countries, democracy only truly consolidated throughout the 20th century through universal suffrage guaranteed with the end of restrictions on political participation based on wealth, education, sex, or race. 6

Contemporary democracies are made up of votes, rights, and reasons. They are not limited to fair procedural rules in the electoral process, but demand respect for substantive fundamental rights of all citizens and a permanent public debate that informs and legitimizes political decisions. 7 To ensure protection of these three aspects, most democratic regimes include in their constitutional framework a supreme court or constitutional court with jurisdiction to arbitrate the inevitable tensions that arise between democracy’s popular sovereignty and constitutionalism’s fundamental rights. 8 These courts are, ultimately, the institutions responsible for protecting fundamental rights and the rules of the democratic game against any abuse of power attempted by the majority. Recent experiences in Hungary, Poland, Turkey, Venezuela, and Nicaragua show that when courts fail to fulfill this role, democracy collapses or suffers major setbacks. 9

In recent years, several events have challenged the prevalence of democratic constitutionalism in many parts of the world, in a phenomenon characterized by many as democratic recession. 10 Even consolidated democracies have endured moments of turmoil and institutional discredit, 11 as the world witnessed the rise of an authoritarian, anti-pluralist, and anti-institutional populist wave posing serious threats to democracy.

Populism can be right-wing or left-wing, 12 but the recent wave has been characterized by the prevalence of right-wing extremism, often racist, xenophobic, misogynistic, and homophobic. 13 While in the past the far left was united through Communist International, today it is the far right that has a major global network. 14 The hallmark of right-wing populism is the division of society into “us” (the pure, decent, conservatives) and “them” (the corrupt, liberal, cosmopolitan elites). 15 Authoritarian populism flows from the unfulfilled promises of democracy for opportunities and prosperity for all. 16 Three aspects undergird this democratic frustration: political (people do not feel represented by the existing electoral systems, political leaders, and democratic institutions); social (stagnation, unemployment, and the rise of inequality); and cultural identity (a conservative reaction to the progressive identity agenda of human rights that prevailed in recent decades with the protection of the fundamental rights of women, African descendants, religious minorities, LGBTQ+ communities, indigenous populations, and the environment). 17

Extremist authoritarian populist regimes often adopt similar strategies to capitalize on the political, social, and cultural identity-based frustrations fueling democratic recessions. These tactics include by-pass or co-optation of the intermediary institutions that mediate the interface between the people and the government, such as the legislature, the press, and civil society. They also involve attacks on supreme courts and constitutional courts and attempts to capture them by appointing submissive judges. 18 The rise of social media potentializes these strategies by creating a free and instantaneous channel of direct communication between populists and their supporters. 19 This unmediated interaction facilitates the use of disinformation campaigns, hate speech, slander, lies, and conspiracy theories as political tools to advance antidemocratic goals. The instantaneous nature of these channels is ripe for impulsive reactions, which facilitate verbal attacks by supporters and polarization, feeding back into the populist discourse. These tactics threaten democracy and free and fair elections because they deceive voters and silence the opposition, distorting public debate. Ultimately, this form of communication undermines the values that justify the special protection of freedom of expression to begin with. The “truth decay” and “fact polarization” that result from these efforts discredit institutions and consequently foster distrust in democracy. 20

III. Internet, Social Media, and Freedom of Expression 21

The third industrial revolution, also known as the technological or digital revolution, has shaped our world today. 22 Some of its main features are the massification of personal computers, the universalization of smartphones and, most importantly, the internet. One of the main byproducts of the digital revolution and the internet was the emergence of social media platforms such as Facebook, Instagram, YouTube, TikTok and messaging applications like WhatsApp and Telegram. We live in a world of apps, algorithms, artificial intelligence, and innovation occurring at breakneck speed where nothing seems truly new for very long. This is the background for the narrative that follows.

A. The Impact of the Internet

The internet revolutionized the world of interpersonal and social communication, exponentially expanded access to information and knowledge, and created a public sphere where anyone can express ideas, opinions, and disseminate facts. 23 Before the internet, one’s participation in public debate was dependent upon the professional press, 24 which investigated facts, abided by standards of journalistic ethics, 25 and was liable for damages if it knowingly or recklessly published untruthful information. 26 There was a baseline of editorial control and civil liability over the quality and veracity of what was published in this medium. This does not mean that it was a perfect world. The number of media outlets was, and continues to be, limited in quantity and perspectives; journalistic companies have their own interests, and not all of them distinguish fact from opinion with the necessary care. Still, there was some degree of control over what became public, and there were costs to the publication of overtly hateful or false speech.

The internet, with the emergence of websites, personal blogs, and social media, revolutionized this status quo. It created open, online communities for user-generated texts, images, videos, and links, published without editorial control and at no cost. This advanced participation in public discourse, diversified sources, and exponentially increased available information. 27 It gave a voice to minorities, civil society, politicians, public agents, and digital influencers, and it allowed demands for equality and democracy to acquire global dimensions. This represented a powerful contribution to political dynamism, resistance to authoritarianism, and stimulation of creativity, scientific knowledge, and commercial exchanges. 28 Increasingly, the most relevant political, social, and cultural communications take place on the internet’s unofficial channels.

However, the rise of social media also led to an increase in the dissemination of abusive and criminal speech. 29 While these platforms did not create mis- or disinformation, hate speech, or speech that attacks democracy, the ability to publish freely, with no editorial control and little to no accountability, increased the prevalence of these types of speech and facilitated its use as a political tool by populist leaders. 30 Additionally, and more fundamentally, platform business models compounded the problem through algorithms that moderate and distribute online content. 31

B. The Role of Algorithms

The ability to participate and be heard in online public discourse is currently defined by the content moderation algorithms of a couple major technology companies. Although digital platforms initially presented themselves as neutral media where users could publish freely, they in fact exercise legislative, executive, and judicial functions because they unilaterally define speech rules in their terms and conditions and their algorithms decide how content is distributed and how these rules are applied. 32

Specifically, digital platforms rely on algorithms for two different functions: recommending content and moderating content. 33 First, a fundamental aspect of the service they offer involves curating the content available to provide each user with a personalized experience and increase time spent online. They resort to deep learning algorithms that monitor every action on the platform, draw from user data, and predict what content will keep a specific user engaged and active based on their prior activity or that of similar users. 34 The transition from a world of information scarcity to a world of information abundance generated fierce competition for user attention—the most valuable resource in the Digital Age. 35 The power to modify a person’s information environment has a direct impact on their behavior and beliefs. Because AI systems can track an individual’s online history, they can tailor specific messages to maximize impact. More importantly, they monitor whether and how the user interacts with the tailored message, using this feedback to influence future content targeting and progressively becoming more effective in shaping behavior. 36 Given that humans engage more with content that is polarizing and provocative, these algorithms elicit powerful emotions, including anger. 37 The power to organize online content therefore directly impacts freedom of expression, pluralism, and democracy. 38

In addition to recommendation systems, platforms rely on algorithms for content moderation, the process of classifying content to determine whether it violates community standards. 39 As mentioned, the growth of social media and its use by people around the world allowed for the spread of lies and criminal acts with little cost and almost no accountability, threatening the stability of even long-standing democracies. Inevitably, digital platforms had to enforce terms and conditions defining the norms of their digital community and moderate speech accordingly. 40 But the potentially infinite amount of content published online means that this control cannot be exercised exclusively by humans.

Content moderation algorithms optimize the scanning of published content to identify violations of community standards or terms of service at scale and apply measures ranging from removal to reducing reach or including clarifications or references to alternative information. Platforms often rely on two algorithmic models for content moderation. The first is the reproduction detection model , which uses unique identifiers to catch reproductions of content previously labeled as undesired. 41 The second system, the predictive model , uses machine learning techniques to identify potential illegalities in new and unclassified content. 42 Machine learning is a subtype of artificial intelligence that extracts patterns in training datasets, capable of learning from data without explicit programming to do so. 43 Although helpful, both models have shortcomings.

The reproduction detection model is inefficient for content such as hate speech and disinformation, where the potential for new and different publications is virtually unlimited and users can deliberately make changes to avoid detection. 44 The predictive model is still limited in its ability to address situations to which it has not been exposed in training, primarily because it lacks the human ability to understand nuance and to factor in contextual considerations that influence the meaning of speech. 45 Additionally, machine learning algorithms rely on data collected from the real world and may embed prejudices or preconceptions, leading to asymmetrical applications of the filter. 46 And because the training data sets are so large, it can be hard to audit them for these biases. 47

Despite these limitations, algorithms will continue to be a crucial resource in content moderation given the scale of online activities. 48 In the last two months of 2020 alone, Facebook applied a content moderation measure to 105 million publications, and Instagram to 35 million. 49 YouTube has 500 hours of video uploaded per minute and removed more than 9.3 million videos. 50 In the first half of 2020, Twitter analyzed complaints related to 12.4 million accounts for potential violations of its rules and took action against 1.9 million. 51 This data supports the claim that human moderation is impossible, and that algorithms are a necessary tool to reduce the spread of illicit and harmful content. On the one hand, holding platforms accountable for occasional errors in these systems would create wrong incentives to abandon algorithms in content moderation with the negative consequence of significantly increasing the spread of undesired speech. 52 On the other hand, broad demands for platforms to implement algorithms to optimize content moderation, or laws that impose very short deadlines to respond to removal requests submitted by users, can create excessive pressure for the use of these imprecise systems on a larger scale. Acknowledging the limitations of this technology is fundamental for precise regulation.

C. Some Undesirable Consequences

One of the most striking impacts of this new informational environment is the exponential increase in the scale of social communications and the circulation of news. Around the world, few newspapers, print publications, and radio stations cross the threshold of having even one million subscribers and listeners. This suggests the majority of these publications have a much smaller audience, possibly in the thousands or tens of thousands of people. 53 Television reaches millions of viewers, although diluted among dozens or hundreds of channels. 54 Facebook, on the other hand, has about 3 billion active users. 55 YouTube has 2.5 billion accounts. 56 WhatsApp, more than 2 billion. 57 The numbers are bewildering. However, and as anticipated, just as the digital revolution democratized access to knowledge, information, and public space, it also introduced negative consequences for democracy that must be addressed. Three of them include:

a) the increased circulation of disinformation, deliberate lying, hate speech, conspiracy theories, attacks on democracy, and inauthentic behavior, made possible by recommendation algorithms that optimize for user engagement and content moderation algorithms that are still incapable of adequately identifying undesirable content;

b) the tribalization of life, with the formation of echo chambers where groups speak only to themselves, reinforcing confirmation bias, 58 making speech progressively more radical, and contributing to polarization and intolerance; and

c) a global crisis in the business model of the professional press. Although social media platforms have become one of the main sources of information, they do not produce their own content. They hire engineers, not reporters, and their interest is engagement, not news. 59 Because advertisers’ spending has migrated away from traditional news publications to technological platforms with broader reaches, the press has suffered from a lack of revenue which has forced hundreds of major publications, national and local, to close their doors or reduce their journalist workforce. 60 But a free and strong press is more than just a private business; it is a pillar for an open and free society. It serves a public interest in the dissemination of facts, news, opinions, and ideas, indispensable preconditions for the informed exercise of citizenship. Knowledge and truth—never absolute, but sincerely sought—are essential elements for the functioning of a constitutional democracy. Citizens need to share a minimum set of common objective facts from which to inform their own judgments. If they cannot accept the same facts, public debate becomes impossible. Intolerance and violence are byproducts of the inability to communicate—hence the importance of “knowledge institutions,” such as universities, research entities, and the institutional press. The value of free press for democracy is illustrated by the fact that in different parts of the world, the press is one of the only private businesses specifically referred to throughout constitutions. Despite its importance for society and democracy, surveys reveal a concerning decline in its prestige. 61

In the beginning of the digital revolution, there was a belief that the internet should be a free, open, and unregulated space in the interest of protecting access to the platform and promoting freedom of expression. Over time, concerns emerged, and a consensus gradually grew for the need for internet regulation. Multiple approaches for regulating the internet were proposed, including: (a) economic, through antitrust legislation, consumer protection, fair taxation, and copyright rules; (b) privacy, through laws restricting collection of user data without consent, especially for content targeting; and (c) targeting inauthentic behavior, content control, and platform liability rules. 62

Devising the proper balance between the indispensable preservation of freedom of expression on the one hand, and the repression of illegal content on social media on the other, is one of the most complex issues of our generation. Freedom of expression is a fundamental right incorporated into virtually all contemporary constitutions and, in many countries, is considered a preferential freedom. Several reasons have been advanced for granting freedom of expression special protection, including its roles: (a) in the search for the possible truth 63 in an open and plural society, 64 as explored above in discussing the importance of the institutional press; (b) as an essential element for democracy 65 because it allows the free circulation of ideas, information, and opinions that inform public opinion and voting; and (c) as an essential element of human dignity, 66 allowing the expression of an individual’s personality.

The regulation of digital platforms cannot undermine these values but must instead aim at its protection and strengthening. However, in the digital age, these same values that historically justified the reinforced protection of freedom of expression can now justify its regulation. As U.N. Secretary-General António Guterres thoughtfully stated, “the ability to cause large-scale disinformation and undermine scientifically established facts is an existential risk to humanity.” 67

Two aspects of the internet business model are particularly problematic for the protection of democracy and free expression. The first is that, although access to most technological platforms and applications is free, users pay for access with their privacy. 68 As Lawrence Lessig observed, we watch television, but the internet watches us. 69 Everything each individual does online is monitored and monetized. Data is the modern gold. 70 Thus, those who pay for the data can more efficiently disseminate their message through targeted ads. As previously mentioned, the power to modify a person’s information environment has a direct impact on behavior and beliefs, especially when messages are tailored to maximize impact on a specific individual. 71

The second aspect is that algorithms are programmed to maximize time spent online. This often leads to the amplification of provocative, radical, and aggressive content. This in turn compromises freedom of expression because, by targeting engagement, algorithms sacrifice the search for truth (with the wide circulation of fake news), democracy (with attacks on institutions and defense of coups and authoritarianism), and human dignity (with offenses, threats, racism, and others). The pursuit of attention and engagement for revenue is not always compatible with the values that underlie the protection of freedom of expression.

IV. A Framework for the Regulation of Social Media

Platform regulation models can be broadly classified into three categories: (a) state or government regulation, through legislation and rules drawing a compulsory, encompassing framework; (b) self-regulation, through rules drafted by platforms themselves and materialized in their terms of use; and (c) regulated self-regulation or coregulation, through standards fixed by the state but which grant platform flexibility in materializing and implementing them. This Essay argues for the third model, with a combination of governmental and private responsibilities. Compliance should be overseen by an independent committee, with the minority of its representatives coming from the government, and the majority coming from the business sector, academia, technology entities, users, and civil society.

The regulatory framework should aim to reduce the asymmetry of information between platforms and users, safeguard the fundamental right to freedom of expression from undue private or state interventions, and protect and strengthen democracy. The current technical limitations of content moderation algorithms explored above and normal substantive disagreement about what content should be considered illegal or harmful suggest that an ideal regulatory model should optimize the balance between the fundamental rights of users and platforms, recognizing that there will always be cases where consensus is unachievable. The focus of regulation should be the development of adequate procedures for content moderation, capable of minimizing errors and legitimizing decisions even when one disagrees with the substantive result. 72 With these premises as background, the proposal for regulation formulated here is divided into three levels: (a) the appropriate intermediary liability model for user-generated content; (b) procedural duties for content moderation; and (c) minimum duties to moderate content that represents concrete threats to democracy and/or freedom of expression itself.

A. Intermediary Liability for User-Generated Content

There are three main regimes for platform liability for third-party content. In strict liability models, platforms are held responsible for all user-generated posts. 73 Since platforms have limited editorial control over what is posted and limited human oversight over the millions of posts made daily, this would be a potentially destructive regime. In knowledge-based liability models, platform liability arises if they do not act to remove content after an extrajudicial request from users—this is also known as a “notice-and-takedown” system. 74 Finally, a third model would make platforms liable for user-generated content only in cases of noncompliance with a court order mandating content removal. This latter model was adopted in Brazil with the Civil Framework for the Internet (Marco Civil da Internet). 75 The only exception in Brazilian legislation to this general rule is revenge porn: if there is a violation of intimacy resulting from the nonconsensual disclosure of images, videos, or other materials containing private nudity or private sexual acts, extrajudicial notification is sufficient to create an obligation for content removal under penalty of liability. 76

In our view, the Brazilian model is the one that most adequately balances the fundamental rights involved. As mentioned, in the most complex cases concerning freedom of expression, people will disagree on the legality of speech. Rules holding platforms accountable for not removing content after mere user notification create incentives for over-removal of any potentially controversial content, excessively restricting users’ freedom of expression. If the state threatens to hold digital platforms accountable if it disagrees with their assessment, companies will have the incentive to remove all content that could potentially be considered illicit by courts to avoid liability. 77

Nonetheless, this liability regime should coexist with a broader regulatory structure imposing principles, limits, and duties on content moderation by digital platforms, both to increase the legitimacy of platforms’ application of their own terms and conditions and to minimize the potentially devastating impacts of illicit or harmful speech.

B. Standards for Proactive Content Moderation

Platforms have free enterprise and freedom of expression rights to set their own rules and decide the kind of environment they want to create, as well as to moderate harmful content that could drive users away. However, because these content moderation algorithms are the new governors of the public sphere, 78 and because they define the ability to participate and be heard in online public discourse, platforms should abide by minimum procedural duties of transparency and auditing, due process, and fairness.

1. Transparency and Auditing

Transparency and auditing measures serve mainly to ensure that platforms are accountable for content moderation decisions and for the impacts of their algorithms. They provide users with greater understanding and knowledge about the extent to which platforms regulate speech, and they provide oversight bodies and researchers with information to understand the threats of digital services and the role of platforms in amplifying or minimizing them.

Driven by demands from civil society, several digital platforms already publish transparency reports. 79 However, the lack of binding standards means that these reports have significant gaps, no independent verification of the information provided, 80 and no standardization across platforms, preventing comparative analysis. 81 In this context, regulatory initiatives that impose minimum requirements and standards are crucial to make oversight more effective. On the other hand, overly broad transparency mandates may force platforms to adopt simpler content moderation rules to reduce costs, which could negatively impact the accuracy of content moderation or the quality of the user experience. 82 A tiered approach to transparency, where certain information is public and certain information is limited to oversight bodies or previously qualified researchers, ensures adequate protection of countervailing interests, such as user privacy and business confidentiality. 83 The Digital Services Act, 84 recently passed in the European Union, contains robust transparency provisions that generally align with these considerations. 85

The information that should be publicly provided includes clear and unambiguous terms of use, the options available to address violations (such as removal, amplification reduction, clarifications, and account suspension) and the division of labor between algorithms and humans. More importantly, public transparency reports should include information on the accuracy of automated moderation measures and the number of content moderation actions broken down by type (such as removal, blocking, and account deletion). 86 There must also be transparency obligations to researchers, giving them access to crucial information and statistics, including to the content analyzed for the content moderation decisions. 87

Although valuable, transparency requirements are insufficient in promoting accountability because they rely on users and researchers to actively monitor platform conduct and presuppose that they have the power to draw attention to flaws and promote changes. 88 Legally mandated third-party algorithmic auditing is therefore an important complement to ensure that these models satisfy legal, ethical, and safety standards and to elucidate the embedded value tradeoffs, such as between user safety and freedom of expression. 89 As a starting point, algorithm audits should consider matters such as how accurately they perform, any potential bias or discrimination incorporated in the data, and to what extent the internal mechanics are explainable to humans. 90 The Digital Services Act contains a similar proposal. 91

The market for algorithmic auditing is still emergent and replete with uncertainty. In attempting to navigate this scenario, regulators should: (a) define how often the audits should happen; (b) develop standards and best practices for auditing procedures; (c) mandate specific disclosure obligations so auditors have access to the required data; and (d) define how identified harms should be addressed. 92

2. Due Process and Fairness

To ensure due process, platforms must inform users affected by content moderation decisions of the allegedly violated provision of the terms of use, as well as offer an internal system of appeals against these decisions. Platforms must also create systems that allow for the substantiated denunciation of content or accounts by other users, and notify reporting users of the decision taken.

As for fairness, platforms should ensure that the rules are applied equally to all users. Although it is reasonable to suppose that platforms may adopt different criteria for public persons or information of public interest, these exceptions must be clear in the terms of use. This issue has recently been the subject of controversy between the Facebook Oversight Board and the company. 93

Due to the enormous amount of content published on the platforms and the inevitability of using automated mechanisms for content moderation, platforms should not be held accountable for a violation of these duties in specific cases, but only when the analysis reveals a systemic failure to comply. 94

C. Minimum Duties to Moderate Illicit Content

The regulatory framework should also contain specific obligations to address certain types of especially harmful speech. The following categories are considered by the authors to fall within this group: disinformation, hate speech, anti-democratic attacks, cyberbullying, terrorism, and child pornography. Admittedly, defining and consensually identifying the speech included in these categories—except in the case of child pornography 95 —is a complex and largely subjective task. Precisely for this reason, platforms should be free to define how the concepts will be operationalized, as long as they guide definitions by international human rights parameters and in a transparent manner. This does not mean that all platforms will reach the same definitions nor the same substantive results in concrete cases, but this should not be considered a flaw in the system, since the plurality of rules promotes freedom of expression. The obligation to observe international human rights parameters reduces the discretion of companies, while allowing for the diversity of policies among them. After defining these categories, platforms must establish mechanisms that allow users to report violations.

In addition, platforms should develop mechanisms to address coordinated inauthentic behaviors, which involve the use of automated systems or deceitful means to artificially amplify false or dangerous messages by using bots, fake profiles, trolls, and provocateurs. 96 For example, if a person publishes a post for his twenty followers saying that kerosene oil is good for curing COVID-19, the negative impact of this misinformation is limited. However, if that message is amplified to thousands of users, a greater public health issue arises. Or, in another example, if the false message that an election was rigged reaches millions of people, there is a democratic risk due to the loss of institutional credibility.

The role of oversight bodies should be to verify that platforms have adopted terms of use that prohibit the sharing of these categories of speech and ensure that, systemically, the recommendation and content moderation systems are trained to moderate this content.

V. Conclusion

The World Wide Web has provided billions of people with access to knowledge, information, and the public space, changing the course of history. However, the misuse of the internet and social media poses serious threats to democracy and fundamental rights. Some degree of regulation has become necessary to confront inauthentic behavior and illegitimate content. It is essential, however, to act with transparency, proportionality, and adequate procedures, so that pluralism, diversity, and freedom of expression are preserved.

In addition to the importance of regulatory action, the responsibility for the preservation of the internet as a healthy public sphere also lies with citizens. Media education and user awareness are fundamental steps for the creation of a free but positive and constructive environment on the internet. Citizens should be conscious that social media can be unfair, perverse, and can violate fundamental rights and basic rules of democracy. They must be attentive not to uncritically pass on all information received. Alongside states, regulators, and tech companies, citizens are also an important force to address these threats. In Jonathan Haidt’s words, “[w]hen our public square is governed by mob dynamics unrestrained by due process, we don’t get justice and inclusion; we get a society that ignores context, proportionality, mercy, and truth.” 97

• 1 Tim Wu, Is the First Amendment Obsolete? , in The Perilous Public Square 15 (David E. Pozen ed., 2020).
• 2 Jack M. Balkin, Free Speech is a Triangle , 118 Colum. L. Rev. 2011, 2019 (2018).
• 3 Luís Roberto Barroso, O Constitucionalismo Democrático ou Neoconstitucionalismo como ideologia vitoriosa do século XX , 4 Revista Publicum 14, 14 (2018).
• 4 Id. at 16.
• 7 Ronald Dworkin, Is Democracy Possible Here?: Principles for a New Political Debate xii (2006); Ronald Dworkin, Taking Rights Seriously 181 (1977).
• 8 Barroso, supra note 3, at 16.
• 9 Samuel Issacharoff, Fragile Democracies: Contested Power in the Era of Constitutional Courts i (2015).
• 10 Larry Diamond, Facing up to the Democratic Recession , 26 J. Democracy 141 (2015). Other scholars have referred to the same phenomenon using other terms, such as democratic retrogression, abusive constitutionalism, competitive authoritarianism, illiberal democracy, and autocratic legalism. See, e.g. , Aziz Huq & Tom Ginsburg, How to Lose a Constitutional Democracy , 65 UCLA L. Rev. 91 (2018); David Landau, Abusive Constitutionalism , 47 U.C. Davis L. Rev. 189 (2013); Kim Lane Scheppele, Autocratic Legalism , 85 U. Chi. L. Rev. 545 (2018).
• 11 Dan Balz, A Year After Jan. 6, Are the Guardrails that Protect Democracy Real or Illusory? , Wash. Post (Jan. 6, 2022), https://perma.cc/633Z-A9AJ; Brexit: Reaction from Around the UK , BBC News (June 24, 2016), https://perma.cc/JHM3-WD7A.
• 12 Cas Mudde, The Populist Zeitgeist , 39 Gov’t & Opposition 541, 549 (2004).
• 13 See generally Mohammed Sinan Siyech, An Introduction to Right-Wing Extremism in India , 33 New Eng. J. Pub. Pol’y 1 (2021) (discussing right-wing extremism in India). See also Eviane Leidig, Hindutva as a Variant of Right-Wing Extremism , 54 Patterns of Prejudice 215 (2020) (tracing the history of “Hindutva”—defined as “an ideology that encompasses a wide range of forms, from violent, paramilitary fringe groups, to organizations that advocate the restoration of Hindu ‘culture’, to mainstream political parties”—and finding that it has become mainstream since 2014 under Modi); Ariel Goldstein, Brazil Leads the Third Wave of the Latin American Far Right , Ctr. for Rsch. on Extremism (Mar. 1, 2021), https://perma.cc/4PCT-NLQJ (discussing right-wing extremism in Brazil under Bolsonaro); Seth G. Jones, The Rise of Far-Right Extremism in the United States , Ctr. for Strategic & Int’l Stud. (Nov. 2018), https://perma.cc/983S-JUA7 (discussing right-wing extremism in the U.S. under Trump).
• 14 Sergio Fausto, O Desafio Democrático [The Democratic Challenge], Piauí (Aug. 2022), https://perma.cc/474A-3849.
• 15 Jan-Werner Muller, Populism and Constitutionalism , in The Oxford Handbook of Populism 590 (Cristóbal Rovira Kaltwasser et al. eds., 2017).
• 16 Ming-Sung Kuo, Against Instantaneous Democracy , 17 Int’l J. Const. L. 554, 558–59 (2019); see also Digital Populism , Eur. Ctr. for Populism Stud., https://perma.cc/D7EV-48MV.
• 17 Luís Roberto Barroso, Technological Revolution, Democratic Recession and Climate Change: The Limits of Law in a Changing World , 18 Int’l J. Const. L. 334, 349 (2020).
• 18 For the use of social media, see Sven Engesser et al., Populism and Social Media: How Politicians Spread a Fragmented Ideology , 20 Info. Commc’n & Soc’y 1109 (2017). For attacks on the press, see WPFD 2021: Attacks on Press Freedom Growing Bolder Amid Rising Authoritarianism , Int’l Press Inst. (Apr. 30, 2021), https://perma.cc/SGN9-55A8. For attacks on the judiciary, see Michael Dichio & Igor Logvinenko, Authoritarian Populism, Courts and Democratic Erosion , Just Sec. (Feb. 11, 2021), https://perma.cc/WZ6J-YG49.
• 19 Kuo, supra note 16, at 558–59; see also Digital Populism , supra note 16.
• 20 Vicki C. Jackson, Knowledge Institutions in Constitutional Democracy: Reflections on “the Press” , 15 J. Media L. 275 (2022).
• 21 Many of the ideas and information on this topic were collected in Luna van Brussel Barroso, Liberdade de Expressão e Democracia na Era Digital: O impacto das mídias sociais no mundo contemporâneo [Freedom of Expression and Democracy in the Digital Era: The Impact of Social Media in the Contemporary World] (2022), which was recently published in Brazil.
• 22 The first industrial revolution is marked by the use of steam as a source of energy in the middle of the 18th century. The second started with the use of electricity and the invention of the internal combustion engine at the turn of the 19th to the 20th century. There are already talks of the fourth industrial revolution as a product of the fusion of technologies that blurs the boundaries among the physical, digital, and biological spheres. See generally Klaus Schwab, The Fourth Industrial Revolution (2017).
• 23 Gregory P. Magarian, The Internet and Social Media , in The Oxford Handbook of Freedom of Speech 350, 351–52 (Adrienne Stone & Frederick Schauer eds., 2021).
• 24 Wu, supra note 1, at 15.
• 25 Journalistic ethics include distinguishing fact from opinion, verifying the veracity of what is published, having no self-interest in the matter being reported, listening to the other side, and rectifying mistakes. For an example of an international journalistic ethics charter, see Global Charter of Ethics for Journalists , Int’l Fed’n of Journalists (June 12, 2019), https://perma.cc/7A2C-JD2S.
• 26 See, e.g. , New York Times Co. v. Sullivan, 376 U.S. 254 (1964).
• 27 Balkin, supra note 2, at 2018.
• 28 Magarian, supra note 23, at 351–52.
• 29 Wu, supra note 1, at 15.
• 30 Magarian, supra note 23, at 357–60.
• 31 Niva Elkin-Koren & Maayan Perel, Speech Contestation by Design: Democratizing Speech Governance by AI , 50 Fla. State U. L. Rev. (forthcoming 2023).
• 32 Thomas E. Kadri & Kate Klonick, Facebook v. Sullivan: Public Figures and Newsworthiness in Online Speech , 93 S. Cal. L. Rev. 37, 94 (2019).
• 33 Elkin-Koren & Perel, supra note 31.
• 34 Chris Meserole, How Do Recommender Systems Work on Digital Platforms? , Brookings Inst.(Sept. 21, 2022), https://perma.cc/H53K-SENM.
• 35 Kris Shaffer, Data versus Democracy: How Big Data Algorithms Shape Opinions and Alter the Course of History xi–xv (2019).
• 36 See generally Stuart Russell, Human Compatible: Artificial Intelligence and the Problem of Control (2019).
• 37 Shaffer, supra note 35, at xi–xv.
• 38 More recently, with the advance of neuroscience, platforms have sharpened their ability to manipulate and change our emotions, feelings and, consequently, our behavior in accordance not with our own interests, but with theirs (or of those who they sell this service to). Kaveh Waddell, Advertisers Want to Mine Your Brain , Axios (June 4, 2019), https://perma.cc/EU85-85WX. In this context, there is already talk of a new fundamental right to cognitive liberty, mental self-determination, or the right to free will. Id .
• 39 Content moderation refers to “systems that classify user generated content based on either matching or prediction, leading to a decision and governance outcome (e.g. removal, geoblocking, account takedown).” Robert Gorwa, Reuben Binns & Christian Katzenbach, Algorithmic Content Moderation: Technical and Political Challenges in the Automation of Platform Governance , 7 Big Data & Soc’y 1, 3 (2020).
• 40 Jack M. Balkin, Free Speech in the Algorithmic Society: Big Data, Private Governance, and New School Speech Regulation , 51 U.C. Davis L. Rev. 1149, 1183 (2018).
• 41 See Carey Shenkman, Dhanaraj Thakur & Emma Llansó, Do You See What I See? Capabilities and Limits of Automated Multimedia Content Analysis 13–16 (May 2021),https://perma.cc/J9MP-7PQ8.
• 42 See id. at 17–21.
• 43 See Michael Wooldridge, A Brief History of Artificial Intelligence: What It Is, Where We Are, and Where We Are Going 63 (2021).

Perceptual hashing has been the primary technology utilized to mitigate the spread of CSAM, since the same materials are often repeatedly shared, and databases of offending content are maintained by institutions like the National Center for Missing and Exploited Children (NCMEC) and its international analogue, the International Centre for Missing & Exploited Children (ICMEC).

• 45 Natural language understanding is undermined by language ambiguity, contextual dependence of words of non-immediate proximity, references, metaphors, and general semantics rules. See Erik J. Larson, The Myth of Artificial Intelligence: Why Computers Can’t Think the Way We Do 52–55 (2021). Language comprehension in fact requires unlimited common-sense knowledge about the actual world, which humans possess and is impossible to code. Id . A case decided by Facebook’s Oversight Board illustrates the point: the company’s predictive filter for combatting pornography removed images from a breast cancer awareness campaign, a clearly legitimate content not meant to be targeted by the algorithm. See Breast Cancer Symptoms and Nudity , Oversight Bd. (2020), https://perma.cc/U9A5-TTTJ. However, based on prior training, the algorithm removed the publication because it detected pornography and was unable to factor the contextual consideration that this was a legitimate health campaign. Id .
• 46 See generally Adriano Koshiyama, Emre Kazim & Philip Treleaven, Algorithm Auditing: Managing the Legal, Ethical, and Technological Risks of Artificial Intelligence, Machine Learning, and Associated Algorithms , 55 Computer 40 (2022).
• 47 Elkin-Koren & Perel, supra note 31.
• 48 Evelyn Douek, Governing Online Speech: From “Posts-as-Trumps” to Proportionality and Probability , 121 Colum. L. Rev. 759, 791 (2021).
• 53 See Martha Minow, Saving the Press: Why the Constitution Calls for Government Action to Preserve Freedom of Speech 20 (2021). For example, the best-selling newspaper in the world, The New York Times , ended the year 2022 with around 10 million subscribers across digital and print. Katie Robertson, The New York Times Company Adds 180,000 Digital Subscribers , N.Y. Times (Nov. 2, 2022), https://perma.cc/93PF-TKC5. The Economist magazine had approximately 1.2 million subscribers in 2022. The Economist Group, Annual Report 2022 24 (2022), https://perma.cc/9HQQ-F7W2. Around the world, publications that reach one million subscribers are rare. These Are the Most Popular Paid Subscription News Websites , World Econ. F. (Apr. 29, 2021), https://perma.cc/L2MK-VPNX.
• 54 Lawrence Lessig, They Don’t Represent Us: Reclaiming Our Democracy 105 (2019).
• 55 Essential Facebook Statistics and Trends for 2023 , Datareportal (Feb. 19, 2023), https://perma.cc/UH33-JHUQ.
• 56 YouTube User Statistics 2023 , Glob. Media Insight (Feb. 27, 2023), https://perma.cc/3H4Y-H83V.
• 57 Brian Dean, WhatsApp 2022 User Statistics: How Many People Use WhatsApp , Backlinko (Jan. 5, 2022), https://perma.cc/S8JX-S7HN.
• 58 Confirmation bias, the tendency to seek out and favor information that reinforces one’s existing beliefs, presents an obstacle to critical thinking. Sachin Modgil et al., A Confirmation Bias View on Social Media Induced Polarisation During COVID-19 , Info. Sys. Frontiers (Nov. 20, 2021).
• 59 Minow, supra note 53, at 2.
• 60 Id. at 3, 11.
• 61 On the importance of the role of the press as an institution of public interest and its “crucial relationship” with democracy, see id. at 35. On the press as a “knowledge institution,” the idea of “institutional press,” and data on the loss of prestige by newspapers and television stations, see Jackson, supra note 20, at 4–5.
• 62 See , e.g. , Jack M. Balkin, How to Regulate (and Not Regulate) Social Media , 1 J. Free Speech L. 71, 89–96 (2021).
• 63 By possible truth we mean that not all claims, opinions and beliefs can be ascertained as true or false. Objective truths are factual and can thus be proven even when controversial—for example, climate change and the effectiveness of vaccines. Subjective truths, on the other hand, derive from individual normative, religious, philosophical, and political views. In a pluralistic world, any conception of freedom of expression must protect individual subjective beliefs.
• 64 Eugene Volokh, In Defense of the Marketplace of Ideas/Search for Truth as a Theory of Free Speech Protection , 97 Va. L. Rev. 595, 595 (May 2011).
• 66 Steven J. Heyman, Free Speech and Human Dignity 2 (2008).
• 67 A Global Dialogue to Guide Regulation Worldwide , UNESCO (Feb. 23, 2023), https://perma.cc/ALK8-HTG3.
• 68 Can We Fix What’s Wrong with Social Media? , Yale L. Sch. News (Aug. 3, 2022), https://perma.cc/MN58-2EVK.
• 69 Lessig, supra note 54, at 105.
• 71 See supra Part III.B.
• 72 Doeuk, supra note 48, at 804–13; see also John Bowers & Jonathan Zittrain, Answering Impossible Questions: Content Governance in an Age of Disinformation , Harv. Kennedy Sch. Misinformation Rev. (Jan. 14, 2020), https://perma.cc/R7WW-8MQX.
• 73 Daphne Keller, Systemic Duties of Care and Intermediary Liability , Ctr. for Internet & Soc’y Blog (May 28, 2020), https://perma.cc/25GU-URGT.
• 75 Decreto No. 12.965, de 23 de abril de 2014, Diário Oficial da União [D.O.U.] de 4.14.2014 (Braz.) art. 19. In order to ensure freedom of expression and prevent censorship, providers of internet applications can only be civilly liable for damages resulting from content generated by third parties if, after specific court order, they do not make arrangements to, in the scope and technical limits of their service and within the indicated time, make unavailable the content identified as infringing, otherwise subject to the applicable legal provisions. Id .
• 76 Id. art. 21. The internet application provider that provides content generated by third parties will be held liable for the violation of intimacy resulting from the disclosure, without authorization of its participants, of images, videos, or other materials containing nude scenes or private sexual acts when, upon receipt of notification by the participant or its legal representative, fail to diligently promote, within the scope and technical limits of its service, the unavailability of this content. Id .
• 77 Balkin, supra note 2, at 2017.
• 78 Kate Klonick, The New Governors: The People, Rules, and Processes Governing Online Speech , 131 Harv. L. Rev. 1598, 1603 (2018).
• 79 Transparency Reporting Index, Access Now (July 2021), https://perma.cc/2TSL-2KLD (cataloguing transparency reporting from companies around the world).
• 80 Hum. Rts. Comm., Rep. of the Special Rapporteur on the promotion and protection of the right to freedom of opinion and expression, ¶¶ 63–66, U.N. Doc A/HRC/32/35 (2016).
• 81 Paddy Leerssen, The Soap Box as a Black Box: Regulating Transparency in Social Media Recommender Systems , 11 Eur. J. L. & Tech. (2020).
• 82 Daphne Keller, Some Humility About Transparency , Ctr. for Internet & Soc’y Blog (Mar. 19, 2021), https://perma.cc/4Y85-BATA.
• 83 Mark MacCarthy, Transparency Requirements for Digital Social Media Platforms: Recommendations for Policy Makers and Industry , Transatlantic Working Grp. (Feb. 12, 2020).
• 84 2022 O.J. (L 277) 1 [hereinafter DSA].
• 85 The DSA was approved by the European Parliament on July 5, 2022, and on October 4, 2022, the European Council gave its final acquiescence to the regulation. Digital Services: Landmark Rules Adopted for a Safer, Open Online Environment , Eur. Parliament (July 5, 2022), https://perma.cc/BZP5-V2B2. The DSA increases transparency and accountability of platforms, by providing, for example, for the obligation of “clear information on content moderation or the use of algorithms for recommending content (so-called recommender systems); users will be able to challenge content moderation decisions.” Id .
• 86 MacCarthy, supra note 83, 19–24.
• 87 To this end, American legislators recently introduced a U.S. Congressional bill that proposes a model for conducting research on the impacts of digital communications in a way that protects user privacy. See Platform Accountability and Transparency Act, S. 5339, 117th Congress (2022). The project mandates that digital platforms share data with researchers previously authorized by the Federal Trade Commission and publicly disclose certain data about content, algorithms, and advertising. Id .
• 88 Yifat Nahmias & Maayan Perel, The Oversight of Content Moderation by AI: Impact Assessment and Their Limitations , 58 Harv. J. on Legis. 145, 154–57 (2021).
• 89 Auditing Algorithms: The Existing Landscape, Role of Regulator and Future Outlook , Digit. Regul. Coop. F. (Sept. 23, 2022), https://perma.cc/7N6W-JNCW.
• 90 See generally Koshiyama et al., supra note 46.
• 91 In Article 37, the DSA provides that digital platforms of a certain size should be accountable, through annual independent auditing, for compliance with the obligations set forth in the Regulation and with any commitment undertaken pursuant to codes of conduct and crisis protocols.
• 92 Digit. Regul. Coop. F., supra note 89.
• 93 In a transparency report published at the end of its first year of operation, the Oversight Board highlighted the inadequacy of the explanations presented by Meta on the operation of a system known as cross-check, which apparently gave some users greater freedom on the platform. In January 2022, Meta explained that the cross-check system grants an additional degree of review to certain content that internal systems mark as violating the platform’s terms of use. Meta submitted a query to the Board on how to improve the functioning of this system and the Board made relevant recommendations. See Oversight Board Published Policy Advisory Opinion on Meta’s Cross-Check Program , Oversight Bd. (Dec. 2022), https://perma.cc/87Z5-L759.
• 94 Evelyn Douek, Content Moderation as Systems Thinking , 136 Harv. L. Rev. 526, 602–03 (2022).
• 95 The illicit nature of child pornography is objectively apprehended and does not implicate the same subjective considerations that the other referenced categories entail. Not surprisingly, several databases have been created to facilitate the moderation of this content. See Ofcom, Overview of Perceptual Hashing Technology 14 (Nov. 22, 2022), https://perma.cc/EJ45-B76X (“Several hash databases to support the detection of known CSAM exist, e.g. the National Center for Missing and Exploited Children (NCMEC) hash database, the Internet Watch Foundation (IWF) hash list and the International Child Sexual Exploitation (ICSE) hash database.”).
• 97 Jonathan Haidt, Why the Past 10 Years of American Life Have Been Uniquely Stupid , Atlantic (Apr. 11, 2022), https://perma.cc/2NXD-32VM.

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• > Social Media and Democracy
• > Introduction

Book contents

• Social Media and Democracy
• SSRC Anxieties of Democracy
• Sponsored by the Social Science Research Council
• Copyright page
• Contributors
• 1 Introduction
• 2 Misinformation, Disinformation, and Online Propaganda
• 3 Social Media, Echo Chambers, and Political Polarization
• 4 Online Hate Speech
• 5 Bots and Computational Propaganda: Automation for Communication and Control
• 6 Online Political Advertising in the United States
• 7 Democratic Creative Destruction? The Effect of a Changing Media Landscape on Democracy
• 8 Misinformation and Its Correction
• 9 Comparative Media Regulation in the United States and Europe
• 10 Facts and Where to Find Them: Empirical Research on Internet Platforms and Content Moderation
• 11 Dealing with Disinformation: Evaluating the Case for Amendment of Section 230 of the Communications Decency Act
• 12 Democratic Transparency in the Platform Society
• 13 Conclusion: The Challenges and Opportunities for Social Media Research

1 - Introduction

Published online by Cambridge University Press:  24 August 2020

The goal of this book is to synthesize the existing research on social media and democracy. We present reviews of the literature on disinformation, polarization, echo chambers, hate speech, bots, political advertising, and new media. In addition, wecanvass the literature on reform proposals to address the widely perceived threats todemocracy. We seek to examine the current state of knowledge on social media anddemocracy, to identify the many knowledge gaps and obstacles to research in this area,and to chart a course for future research. We hope to advocate for this new field ofstudy and to suggest that universities, foundations, private firms, and governmentsshould commit to funding and supporting this research.

Widespread concern about the effects of social media on democracy has led to an explosion in research over the last five years. This research comes from disparate corners of academia: departments of political science, psychology, law, communication, economics, and computer science, alongside new initiatives in data science and even artificial intelligence. A new field is forming, and it is time to take stock of what we know, what we need to know, and how we might find it out. That is the purpose of this book.

Of course, research on the impact of technology, in general, and the Internet, in particular, on democracy is not new. The early utopianism of the Internet proffered a theory of “liberation technology ” – a mode of unimpeded, transnational communication that would disrupt authoritarian regimes and promote freedom around the world. Similarly, research exists on the impact of this new technology focused on phenomena such as small donor fundraising, online community building, and the subversive use of the Internet in protests and campaigns, in both democratic and nondemocratic regimes. However, early research was scant and far from systematic, as it tended to rely on studies of blogs or individual campaigns. Yet, to the extent the research hinted at some normative argument as to the Internet’s potential, it largely pointed in a prodemocratic direction .

The 2016 presidential election in the United States and, to a lesser extent, the Brexit referendum earlier that year in the United Kingdom, changed the received wisdom. Looking for an explanation for those surprising results, many turned to the new technology of political communication. Blame was (and continues to be) cast on bots, foreign election interference, online disinformation, targeted ads, echo chambers, and related phenomena. Indeed, since 2016, analysis of any election, social movement, populist victory, or instance of political violence will almost inevitably include some assessment of the role of new technology in determining winners and losers.

As conventional wisdom concerning the effect of the Internet on democracy abruptly shifted, so too did much of the research. That shift was not uniform; in fact, one might say that two camps have emerged. The first emphasizes the rise of social media echo chambers, fake news, hate speech, “computational propaganda,” authoritarian governments’ online targeting of opponents, threats to journalism, and foreign election interference. The other school challenges the independent significance of the shift in technology (as opposed to other sociological factors) while also suggesting that the magnitude and prevalence of the alleged technology-related problems are overblown.

The goal of this book is to synthesize the existing research on social media and democracy. We present reviews of the literature on disinformation, polarization, echo chambers, hate speech, bots, political advertising, and new media. In addition, we canvass the literature on reform proposals to address the widely perceived threats to democracy. We seek to examine the current state of knowledge on social media and democracy, to identify the many knowledge gaps and obstacles to research in this area, and to chart a course for future research. We hope to advocate for this new field of study and to suggest that universities, foundations, private firms, and governments should commit to funding and supporting this research .

We have also made a deliberate choice, which might be jarring to some readers, to include both scientific analysis and policy discussion in a single volume. We made this choice consciously because we worry that the policy community and the scientific community are not speaking to one another enough. We are concerned that reliance on untested conventional wisdom based on folk theories of technology’s impact on democracy is leading to misguided reform proposals that may even worsen the problems they are attempting to solve. Conversely, we think the academics studying online harms are often uninformed about the legal regime in which the internet platforms operate. Rules relating to content moderation, antitrust, political advertising, and other domains of online speech structure the environment in which the alleged online harms of disinformation, polarization, and hate speech manifest. Social scientists need to appreciate the policy context, and policymakers need to understand the current state of knowledge regarding the harms they seek to manage through legislation and regulation .

We should also emphasize that the development of this field has become even more urgent as the Covid-19 pandemic further transforms the online information ecosystem. We undertook the research for this book in the year before the pandemic hit, but the topic has only grown in significance since then. Concerns about Covid-19–related disinformation, as well as how the platforms and governments have responded, have only increased as a result of the pandemic. Forced to stay at home, the mass public has, if anything, become even more dependent on platforms, such as Facebook, Google, and now Zoom, for information and communication services. As we write this, the platforms appear to be taking extraordinary measures against online speech deemed dangerous to public health and safety, but it remains to be seen whether the Covid-specific responses represent a new normal in regulation of disinformation. Either way, the need for empirically grounded understandings of the changing dynamics of online communication to inform public policy in this arena has become, if anything, even more important.

Summary of Chapters

This book should be read with the goal in mind of providing an empirical foundation for sound public policy. The first half of the volume contains literature reviews on the central empirical questions surrounding social media and democracy: disinformation, polarization/echo chambers, hate speech, political advertising, bots/computational propaganda, and the changing landscape for journalism and mass media. The second half surveys reform proposals for both the platforms and governments: measures to correct misinformation, reforms of intermediary liability rules for platforms, comparative media regulation, and transparency measures. To be sure, the chapters do not completely cover the landscape of either “the problem” or the potential “solutions,” but we hope that the volume provides a good introduction for those interested in understanding this emerging field.

In Chapter 2 , Princeton professor Andrew M. Guess and University of Utah professor Benjamin A. Lyons survey the literature on online disinformation. As with all scholars in this field, they grapple with the difficulty of defining disinformation. How we define the problem significantly affects the observed prevalence of disinformation. They caution against attributing widespread beliefs in falsehoods predominantly to social media. However, they present their best estimates from available research as to how much disinformation exists, who produces it, and who consumes it.

In Chapter 3 , Pablo Barberá, formerly a professor at the London School of Economics (when he wrote this chapter) but now a research scientist at Facebook and a professor at the University of Southern California, examines the topic of echo chambers and polarization. A conventional view of the problem posits that, given the explosion of online media sources, people are now able to opt into homogeneous media ecosystems, preselected to reinforce their prior beliefs. As a result, people today are less likely to share a common narrative of facts and news, because they exist in segregated “filter bubbles” or “information cocoons,” particularly on social media. The results for politics are pernicious as compromise becomes less possible and election campaigns rely on mobilizing dramatically different bases rather than attempting to persuade moderate voters. The research Barberá surveys challenges this conventional view, however. Cross-cutting interactions on social media and exposure to diverse sources of news are at least as common as they are in the offline world and, in many cases, more likely. Ranking algorithms, often blamed for serving users what they want to see, do not appear to have as dramatic an effect on polarization as once assumed. Some people may live in segregated online news enclaves, but they appear to be a smaller share of the population than expected, at least in the Western democracies that form the bulk of examples in existing research.

A related issue to polarization is online hate speech, a topic covered in Chapter 4 by Alexandra A. Siegel, a professor at the University of Colorado Boulder. She, too, grapples with the definition of the problem of concern, as have courts, policymakers, and the internet platforms themselves, which have tried to walk the difficult line between unprotected hate speech and permissible expression. Although a large share of users report experiences with online hate speech or harassment (however defined), for only a small share does it comprise a significant amount of the speech viewed on the mainstream platforms. Of course, for some users, such as journalists or high-profile speakers who are targeted, hate speech and threats will comprise a larger share of the communication they view. Moreover, on some platforms, such as Reddit, 4chan or 8chan, avowedly racist echo chambers can flourish. Siegel concludes by surveying studies that look at how online hate speech leads to changes in attitudes, as well as offline hate crimes, and then examines measures that have been successful in combating such speech.

In Chapter 5 , Samuel C. Woolley, a professor at the University of Texas at Austin examines the role of bots and computational propaganda. As he notes, bots are simply “online software programs that run automated tasks.” They can be used for good or ill and are responsible for roughly half of online traffic. When it comes to political bots, though, he notes that they are ordinarily developed to deceive – that is, to trick both users who read their messages and algorithms that can be manipulated to grant undeserved popularity to certain topics or accounts. He notes how bots are now used to intimidate elites and social groups, as well as to spread disinformation. The Internet’s privileging of anonymity and automation is what gives political bots their power. Perhaps more than any other chapter in the volume, this discussion of bots isolates how new technology, itself, places stress on democracies. Whereas previous generations of media experienced disinformation, polarization, and hate speech, bots are a unique feature of the Internet Age.

Chapter 6 , by Wesleyan professor Erika Franklin Fowler, Bowdoin College professor Michael M. Franz, and Washington State professor Travis N. Ridout, covers political advertising. It pays particular attention to the United States, since it is responsible for more political advertising than any other country in the world by orders of magnitude. The authors detail the regulatory vacuum into which online ads fall. As a result, the infamous Russian purchase of ads in the 2016 US presidential campaign should not be seen as such a surprise, given the absence of effective law governing online ads, especially so-called issue ads that discuss controversial topics rather than supporting or opposing particular candidates. The data on online advertising have been scarce until recently. Following the 2016 election, Google, Twitter, and Facebook all developed different ad archives that provide for greater transparency than the law requires and will allow scholars to analyze political advertising going forward. The authors present what data we have from previous elections, while highlighting the need for more detailed data from the platforms.

In Chapter 7 , Professor Rasmus Kleis Nielsen and Richard Fletcher of the Reuters Institute at Oxford review the literature on the implications of the transition to online media and journalism for democracy. They describe the impact of digital and mobile technologies on news organizations as a kind of “creative destruction.” They show that the decline of newspapers started well before the rise of the Internet, but digital technologies have accelerated their decline. Websites destroyed the market for classified ads, which had been the lifeblood of local newspapers, but Google and Facebook have gained a duopoly on online advertising. Those firms free ride off the content produced by publishers while competing against those same publishers for advertising dollars. At the same time as the platforms are “disrupting” the business model for news, defining who or what constitutes “the news” or “the media” becomes complicated in the Internet Age, when anyone can blog, tweet, or post. The authors also note that news audiences have moved from a system of “direct discovery,” in which audiences intentionally visit or receive the news from the original source, to “distributed discovery,” in which the audience receives the news from “search engines, social media, and other platform products.” The “automated serendipity” produced by search engines and social media leads online audiences to gain exposure to more sources of news than they would if limited to offline sources. The authors conclude that the rise of online news undermines established institutions of twentieth-century democracy, such as political parties, legacy media, and member-based interest groups, but that a new, more democratic media environment has benefits as well.

In Chapter 8 , Chloe Wittenberg and Professor Adam J. Berinsky, both of MIT, discuss the different ways to correct misinformation. Their chapter is humbling, in large part because they describe how difficult it is to correct misinformation. Merely correcting misinformation with disclaimers or counter-speech rarely erases the false belief. Because of motivated reasoning and other factors, countering misinformation may backfire for some people and even reinforce false beliefs. They note that the most effective responses to misinformation require corrections from a source the believers trust, delivered in a way that affirms their worldview. As such, the context in which misinformation arises and different qualities of the person who engages with it will often determine how strategies must be tailored to address false beliefs.

In Chapter 9 , the first of the “policy” or “reform” chapters, Stanford professor Francis Fukuyama and Andrew Grotto, director of the Stanford Program on Geopolitics, Technology, and Governance, focus on how different countries regulate legacy media, with an eye to how they might regulate the Internet and social media. Some countries, such as France, Germany, and Great Britain, have a long and robust tradition of public broadcasting. Always suspicious of centralized authority, the United States, in contrast, was late to establish a Corporation for Public Broadcasting, which never attained the power and popularity of its European counterparts. Southern European countries and many former Communist countries have found themselves with an oligarchic model of media regulation – the most extreme form found in Italy during Silvio Berlusconi’s monopolistic reign. The authors note that traditions in Europe with respect to broadcast regulation flow over into regulation of the Internet. In France, for example, the Macron government has established expedited procedures to deal with misinformation and ordered platforms to take down offending content. Germany, quite famously, passed the NetzDG, which makes internet platforms liable for certain illegal speech that occurs on their platform after they have been warned. For similar regulation to arise in the United States, old legal tools, such as the Fairness Doctrine or “must carry” provisions, or new conceptions of antitrust, would need to be developed to rein in the power of the platforms.

In Chapter 10 , Daphne Keller, director of the Program on Platform Regulation at the Stanford Cyber Policy Center, and Paddy Leerssen, PhD candidate at the University of Amsterdam, review the literature on government and platform takedown of internet content. The authors point to the available data published by governments, academics, nongovernmental organizations (NGOs) and the platforms themselves as to how much content they take down and for which reasons. They also describe how laws that fail to factor in the operational realities of “notice and takedown” systems can have the effect of causing platforms to overcensor in order to avoid legal liability. Much remains to be learned as to the platforms’ takedown of content based on either their community standards or legal obligations; but, from the available literature, Keller and Leerssen warn of high rates of false positives in both filtering and human review of content. Moreover, in the face of vague legal directives, platforms tend to overcensor to avoid liability, a finding that takes on added urgency in view of President Trump’s May 2020 Executive Order on Preventing Online Censorship. The authors examine the range of takedowns from hate speech and intellectual property violations to terrorist content and the “right to be forgotten.”

Tim Hwang, research fellow at the Center for Security and Emerging Technology at Georgetown University, deals with similar issues in Chapter 11 , “Dealing with Disinformation: Evaluating the Case for Amendment of Section 230 of the Communications Decency Act.” The chapter considers what, if any, amendments should be made to section 230 of the Communications Decency Act (CDA) in the name of protecting against disinformation. Hailed as a cornerstone of the free Internet, that legal provision largely immunizes platforms from liability for the speech of outsiders present on their sites, while also encouraging platforms to take action against certain categories of objectionable content. Hwang argues that confronting the problem of disinformation does not require undermining the core components of CDA 230. Ancillary regulations concerning transparency, bots, advertising, microtargeting, or a kind of “net neutrality” for platforms would not require changing section 230 but could be legislated independently. Hwang warns about changing the intermediary liability rules in section 230. Like Keller and Leerssen, he worries that platforms might overcorrect, take down more speech than required, and become less transparent.

In Chapter 12 , Robert Gorwa and Professor Timothy Garton Ash, both of Oxford University, examine reforms to promote transparency of the platforms. They describe the various voluntary transparency reports regarding takedowns and takedown requests from governments, as laid out by the Global Network Initiative (created by firms and civil society groups). In addition, local regulations or platform-specific legal actions (such as Facebook’s 2011 Federal Trade Commission [FTC] Consent Decree) may require additional transparency from the platforms. They detail how different platforms have adopted different transparency rules for their community standards, takedowns, advertising, and other domains. Most notable is Facebook’s recent innovation in creating an outside oversight board that will hear appeals from content takedowns. Moreover, third parties have embarked on certain transparency efforts, such as when ProPublica attempted to crowdsource political advertisements on Facebook. They conclude with warnings about how some transparency measures, if poorly tailored, do little to further openness and better understanding of platform practices and can even backfire depending on how companies adapt to these new rules.

Chapter 13 presents a conclusion in which we discuss “The Challenges and Opportunities for Social Media Research.” In particular, we stress the importance of access to new forms of data for public-facing research and note the new legal and ethical questions arising from research in this domain. We have entered a new world for research on fundamental questions of political communication and behavior. Although the amount of data now available for research in these areas is unprecedented, large companies control access to most of the data that contain the answers to the questions social scientists are now asking. How social scientists interact with these companies, let alone whether to accept funding and exclusive data access from them, has become a unique challenge for modern research. Moreover, privacy concerns, especially in the wake of the notorious Cambridge Analytica scandal, have led the major internet platforms to become increasingly restrictive of data access for researchers. In the name of protecting privacy, governments have clamped down as well, with laws such as the European General Data Protection Regulation (GDPR). Although GDPR includes an exception for research, lawyers at the platforms have interpreted the exception narrowly and continue to raise privacy objections as a significant barrier to research. Nevertheless, the importance of greater data access for analysts who produce research placed in the public domain (as opposed to internal researchers who work for the platforms) has never been greater. The many policy suggestions discussed in the second half of this volume require rigorous scientific research to inform their advocacy and implementation. How scholars will navigate this new research terrain remains an open question, but we hope this volume serves as a clarion call for regulators, firms, funders, and the research community to provide an answer.

Importance of the Research

Despite the limitations to data access all acknowledge, the authors in this volume present certain important insights as to the effect of new communication technologies on democracy. First and foremost, it is now beyond doubt that the way in which citizens consume information about politics – and, consequently, the way in which elites produce information for citizens – has fundamentally changed over the past decade. Moreover, the landscape of political communication is in great flux – so much so that the research presented here will need to be updated in short order as new platforms emerge, existing platforms adopt new policies, and political actors adapt to the dynamics we identify.

Second, we need to update more frequently our substantive understanding of how people are exposed to and process political information. In other words, the questions addressed in the following chapters are important if we want to understand the functioning of politics in the current moment. Several chapters discuss issues that are genuinely new, such as Chapter 5 by Woolley on bots (social media accounts that produce content via automated algorithms) and Chapter 7 by Nielsen and Fletcher on the impact of the digital revolution on the media industry and individuals’ news consumption. Others represent new takes on old questions, such as Chapter 4 by Siegel on hate speech and Chapter 2 by Guess and Lyons on political disinformation. Still others consider old questions that need to be considered anew in the digital environment: Chapter 3 by Barberá on political polarization, here considered in the context of social media usage, and Chapter 8 by Wittenberg and Berinsky on correcting misinformation.

Third, we find ourselves in a moment where there has been a radical transformation in the way we can actually study political activity employing both qualitative and, especially, quantitative analysis. The momentous development here has been the emergence of digital trace data – that is, digital records that are left behind from human activity that can subsequently be analyzed. Indeed, it is difficult to think of many aspects of day-to-day life that do not leave behind digital trace data given the ubiquity of smartphones and internet access – to say nothing of electronic locks, credit cards, and digitized transportation records (a feature of modern life that may become all the more important in the “contact tracing” world of Covid-19). For political science, however, the rise of social media may be the most transformative of all. For the first time in human history, we have real time records of millions – if not billions – of people as they discuss politics, share information about politics, and organize politically. Each of these actions simultaneously produces an archived, digitized record. We are also living through a period of time in which great strides have been made in how to extract information from enormous collections of electronic data generally (machine learning) and how to use statistical methods to analyze text (natural language processing and other text-as-data tools). Taken together, these developments have unlocked whole new methods of studying politics and political behavior. Taking stock of what we can learn, have learned, and should be able to learn from these new methods of analysis is therefore important. Collectively, the research summarized in the ensuing chapters provides a window into these developments insofar as they pertain to social media and politics .

Finally, in the post-2016 US presidential election, post–Cambridge Analytica era, there has been tremendous pressure on policymakers to “do something” about many of the topics discussed in this volume. This pressure, however, presents a serious challenge in view of one of the primary conclusions of this volume: We are only scratching the surface of what we know about many of these phenomena. To present just one example, there is a great desire to design interventions to reduce the spread of fake news. Yet, if we do not know who shares fake news, why they share fake news, or even whether they are sharing fake news because they think it is true or, instead, because they agree with it ideologically and do not care if it is fake, how can we design appropriate policy to reduce its spread? Moreover, if we do not know the effects of exposure to fake news, then we cannot know the “benefits” of reducing exposure to it. Because everyone recognizes the potential harms of empowering companies, such as Facebook and Google, to be “arbiters of truth,” the benefits of reducing exposure to fake news must be considerable to justify ceding that kind of power over the speech marketplace to profit-maximizing American companies. Thus, the kind of research that we report throughout this volume has a crucial role to play in informing policy decision-making. We hope that by gathering so much of it in one place, we can make it accessible for policymakers considering reform options. We also hope that by candidly expressing how little we know about the dynamics of social media and democracy in some domains, we issue a note of caution to reformers seeking, hastily and prematurely, “to do something or anything” before we understand the nature of the problems that need solving .

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• Introduction
• By Nathaniel Persily , Joshua A. Tucker
• Edited by Nathaniel Persily , Stanford University, California , Joshua A. Tucker , New York University
• Book: Social Media and Democracy
• Online publication: 24 August 2020

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Social media, disinformation, and democracy: how different types of social media usage affect democracy cross-nationally

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• https://doi.org/10.1080/13510347.2023.2208355

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Much speculation exists regarding how social media impacts the health of democracies. However, minimal scholarly research empirically examines the effect social media has on democracy across multiple states and regions. Thus, this article analyses the effect social media and disinformation transmitted over social media have on democracy. The findings from a cross-national, time-series analysis of 158 states from 2000–2019 indicate that different types of social media usage have varying effects on democracy. General social media consumption, the presence of diverse political viewpoints on social media, and the use of social media in political campaigns bolster democracy. However, social media disinformation, online political polarization, and the use of social media to organize offline violence reduce overall levels of democracy. In addition, a mediation analysis is conducted to identify the precise linkages between social media disinformation and democracy and indicates that government and political party disinformation impact democracy by weakening key democratic norms.

• Social media
• disinformation
• political polarization
• democratic norms

Disclosure statement

No potential conflict of interest was reported by the author(s).

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9 Ceron, and Memoli, “Flames and Debates,” 225–40.

10 Barberá, “How Social Media Reduces Mass Political Polarization,” 1–46.

11 Best, and Wade. “The Internet and Democracy,” 255–71; Pirannejad, “Can the Internet Promote Democracy?,” 281–95.

12 Ceron and Memoli, “Flames and Debate”.

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14 Norris, “The Impact of Social Media on the Arab Uprisings”; Weare, “The Internet and Democracy,” 659–91.

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20 Cheibub et al., “What makes Democracies Endure?,” 39–55; Przeworski, and Limongi, “Political Regimes and Economic Growth,” 51–69; Przeworski, and Limongi. “Modernization,” 155–83.

21 Diamond, “Rethinking Civil Society,” 4–17; Leonardi, Nanetti, and Putnam. Making Democracy Work ; Zmerli, and Newton, “Social Trust and Attitudes Toward Democracy,” 706–24.

22 Bobba, and Coviello, “Weak Instruments and Weak Identification, in Estimating the Effects of Education, on Democracy,”301–6; Castelló-Climent, “On the Distribution of Education and Democracy,” 179–90.

23 Allen, “Social Media’s Growing Impact on Our Lives.”

24 Olaniran, and Williams, “Social Media Effects,” 77–94; Kramer, Guillory, and Hancock, “Experimental Evidence of Massive-Scale Emotional Contagion through Social Networks,” 8788–90.

25 Jha and Kodila-Tedika, “Does Social Media.”

26 Barberá, “How Social Media Reduces.”

28 Parkyn, “The Role of Social Media in Development.”

29 McCoy, and Scully, “Deliberative Dialogue to Expand Civic Engagement,” 117–35.

30 Boulianne, “Social Media Use”; Chen, “Social Media Use”.

31 Bode, “Gateway Political Behaviors”.

32 Bode, “Political News”.

33 Swigger, “The Online Citizen”.

34 Haenschen. “Social Pressure”.

35 Bjørnskov, Christian, and Martin Rode, “Regime Types and Regime Change,” 531–51.

36 Diamond, “Rethinking Civil Society”; Leonardi, “Making Democracy Work”; Putnam, Bowling Alone . Simon and Schuster; Zmerli, Sonja, and Ken Newton, “Social Trust and Attitudes Toward Democracy.” Public Opinion Quarterly , 706–24.

37 Cooley, “Authoritarianism Goes Global.” 49–63.

38 Diamond, “Rethinking Civil Society”; Leonardi, “Making Democracy Work”; Putnam, “Bowling Alone”.

39 Clayton et al., “Elite Rhetoric can Undermine Democratic Norms,” e2024125118.

40 Albertson, and Guiler, “Conspiracy Theories, Election Rigging, and Support for Democratic Norms,” 2053168020959859.

41 Albertson, “Conspiracy Theories”; Clayton, “Elite Rhetoric”; Colomina et al.,. “The Impact of Disinformation on Democratic Processes and Human Rights in the World.”; McKay, and Tenove. “Disinformation as a Threat to Deliberative Democracy.” 703–17; Olaniran, and Williams. “Social Media Effects,” 77-94.

42 Piazza, “Fake News,” 55–77.

43 Albertson, “Conspiracy Theories”; Miller, Saunders, and Farhart, “Conspiracy Endorsement as Motivated Reasoning.” 824–44.

44 Boese, “State of the World”; Svolik, “Polarization versus Democracy,” 20–32.

45 Jeppesen et al., The Capitol Riots ; Samuelson. “Why were the Police Attacked on January 6th?.”

46 Howe, “Eroding Norms and Democratic Deconsolidation,”15–29; Olaniran, and Williams, “Social Media Effects.” 77–94.

47 Data Reportal, “Global Overview”.

48 Index, Global Web. “Social Media Marketing Trends in.”

49 Ng, Cruickshank, and Carley. “Cross-Platform Information Spread during the January 6th Capitol Riots,” 133.

50 Golovchenko et al., “Cross-Platform State Propaganda.” 357–89; Ng, “Cross-Platform Information”; Magelinski, Ng, and Carley, “A Synchronized Action Framework for Detection of Coordination on Social Media.”; Pierri, Artoni, and Ceri, “Investigating Italian Disinformation Spreading on Twitter in the Context of 2019 European Elections,” e0227821.

51 Frenkel, and Alba, “In India, Facebook Grapples with an Amplified Version of Its Problems.”; Witness, Global, “Algorithm of Harm.”

52 Frenkel, “In India”; Jeppesen; “The Capitol Riots”; Sombatpoonsiri, “Two Thailands,” 67–79.

53 Plattner, and Diamond, “Liberation Technology.”

54 Coppedge et al., V-Dem Codebook v11. Varieties of Democracy (VDem) Project .

55 Electoral democracy refers to the extent that leaders are responsive to citizens through the mechanism of free and fair elections and suffrage is extensive. For a more detailed description of the variable please see the VDEM codebook (page 43).

56 Liberal democracy refers to the extent that the state protects individual liberties and minority rights. Participatory democracy refers to the extent there is participation by citizens in political processes, including both electoral and non-electoral. Deliberative democracy refers to the extent democratic processes guide decision making within the state. Egalitarian democracy refers to the extent rights and freedoms are present across society, resources are distributed equally across society, and individuals and groups have equal access to power (Coppedge et al. 2021). For a full description of each variable please see the VDEM codebook (pages 43-46).

57 Hunter, Biglaiser, McGauvran, and Collins, “The Effects of Social Media on Domestic Terrorism,” 1–25; Piazza, “Fake News”.

58 The variables government and political party disinformation, online media fractionalization, and social media violence are reverse coded from their original coding scheme for clarity of interpretation purposes.

59 Wilson, “Measuring Internet & Politics.”; Pemstein et al., “The V-Dem Measurement Model.”.

60 Boese, “How (Not) to Measure Democracy.” 95–127; Lin et al., “Government-Sponsored Disinformation and the Severity of Respiratory Infection Epidemics Including COVID-19,” 114744; Wilson and Wiysonge, “Social Media and Vaccine Hesitancy,” 1.

61 Boese, “State of the Word”; Ijioma, and Nze, “Evaluating the Influence of Social Media Use in COVID-19 Vaccine Hesitancy of Residents of Owerri Metropolis,” 10–24; Knuutila, Neudert, and Howard, “Who is Afraid of Fake News?”; Krieger. “Democracy and the Quality of Economic Institutions,” 357–76; McMann, “Measuring Subnational Democracy,” 19–37; Piazza, “Fake News”; Kellam and Berlucchi, “Who’s to Blame for Democratic Backsliding,” 1–21.

62 DataReportal, “Global Overview”.

63 Cuello-Garcia, Pérez-Gaxiola, and van Amelsvoort, “Social Media can have an Impact on How We Manage and Investigate the COVID-19 Pandemic,” 198–201; Kutlu. “Analysis of Dermatologic Conditions in Turkey and Italy by Using Google Trends Analysis in the Era of the COVID-19 Pandemic,” e13949; Kwanda, and Lin, “Fake news Practices in Indonesian Newsrooms during and After the Palu Earthquake,” 849–66; Rodrigues and Xu, “<? covid19?> Regulation of COVID-19 Fake News Infodemic in China and India,” 125–31.

64 World Bank. World Development Indicators.

65 Beck, and Katz, “What to do (and not to do) with Time-Series Cross-Section Data,” 634–47.

66 Beck and Katz, “Random Coefficient Models for Time-series—cross-section Data,” 182–95.

67 Achen, “Why Lagged Dependent Variables can Suppress the Explanatory Power of Other Independent Variables,” 7–2000.

68 Bernauer, and Kuhn. “Is there an Environmental version of the Kantian Peace?,” 77–102.

69 Wooldridge, Econometric Analysis of Cross Section and Panel Data .

70 Martin, “Towards an Explanation of Electoral Rules Change,” 169–191, 186.

71 Avelino, Brown, and Hunter. “The Effects of Capital Mobility, Trade Openness, and Democracy on Social Spending in Latin America, 1980–1999,” 625–41; Bernauer, “Is There an Environmental”; Brown and Hunter, “Democracy and Human Capital Formation,” 842–64; Gray. “International Organization as a Seal of Approval,” 931–49; Hossain. “Foreign Direct Investment, Economic Freedom and Economic Growth,” 200–14; Jorgenson, “Political-Economic Integration, Industrial Pollution and Human Health,” 115–43; Saideman et al., “Democratization, Political Institutions, and Ethnic Conflict,” 103–29; Thames and Williams, “Incentives for Personal Votes and Women’s Representation in Legislatures,” 1575–600.

72 Freedom House (2018). https://freedomhouse.org/

73 The variables government and political party disinformation, online media fractionalization, and social media violence are reverse coded from their original coding scheme for clarity of interpretation purposes.

74 Barro and Lee, “A New Data Set of Educational Attainment in the World, 1950–2010,” 184–98; In order to account for some missing data in the Barro-Lee educational dataset we impute data for the educational data. We use a common technique advanced in other research to impute the Barro-Lee educational data We used a multiple imputation strategy (M = 5) to predict missing values of the Barr-Lee educational data. As is consistent in previous research, we used the dependent variable (democracy) in our imputation equation and no values were imputed for the democracy dependent variable. In addition, given the use of imputed data, prais winsten regressions could not be used and fixed effects estimations were employed.

75 Norton and Tomal. “Religion and Female Educational Attainment,” 961–86; Ziesemer,“Global Dynamics of Gini Coefficients of education for 146 Countries,” 85–95.

76 Ahmed et al., “Measuring the Efficiency of Health Systems in Asia,” e022155; Lo Bue and Klasen, “Identifying Synergies and Complementarities between MDGs,” 647–70; Wamala, “Completion of a Full Course of Primary Schooling among All Children Everywhere by 2015,” 147–54.

77 Due to space constraints these results are available upon request.

78 Ibid; UNESCO: Institute for Statistics. (2023). “Data for the Sustainable Development Goals.” https://uis.unesco.org/

79 Due to space constraints these results are available upon request.

81 Baron and Kenny. “The Moderator–mediator Variable Distinction in Social Psychological Research,” 1173; Little et al., “Structural Equation Modeling of Mediation and Moderation with Contextual Factors,” 207–30; Hoyle, ed. Handbook of Structural Equation Modeling .

82 Baron, “The Moderator-Mediator”; Imai, Keele, and Yamamoto. “Identification, Inference and Sensitivity Analysis for Causal Mediation Effects,” 51–71.

83 Jo, “Causal Inference in Randomized Experiments with Mediational Processes,” 314.

84 Each model contains the control variables found in the primary models. Due to space constraints, tables displaying the full results of all control variables are available upon request. The dependent variable in each model is electoral democracy. Data for all mediating variables were collected from the VDEM. The variables disengaged society, disrespect for counterarguments, denial of elections results, and general political violence are reverse coded from their original coding scheme for clarity of interpretation purposes.

85 Boese, “State of the World”.

86 Le Gallo and Páez. “Using Synthetic Variables in Instrumental Variable Estimation of Spatial Series Models,” 2227–42.

87 The data for the internet penetration variable was collected from the World Bank (2020). The government disinformation abroad variable is interacted with internet penetration for the models examining government disinformation and democracy. The party disinformation abroad variable is interacted with internet penetration in models examining party disinformation and democracy. The government and party disinformation abroad measures were both collected from (VDEM). Higher values indicate greater disinformation. The variables are reverse coded from their original coding scheme for clarity of interpretation purposes.

88 The measure is taken from the DSP.

89 In the estimations, we include the control variables found in the previous models, our primary instruments, a lagged operator (lagged dependent variable), robust standard errors, and an AR(1) specification to control for A1 serial correlation. These techniques are common in studies that employ GMM system and GMM difference estimations; Heid, Langer, and Larch, “Income and Democracy,” 166–9.

90 Due to space constraints these models are available upon request.

91 This measure is taken from the DSP.

92 Bode, “Political News”; Chen, “Social Media Use”; Haenschen. “Social Pressure”; Swigger, “The Online Citizen”.

93 Golovchenko, “Cross Platform”; Ng, “Cross-Platform”; Pierri, “Investigating Italian”.

94 Aday et al., “New Media and Conflict after the Arab Spring,” 1–24; Plattner, “Liberation Technology”; Morozov, “The Net Delusion”.

Additional information

Notes on contributors, lance y. hunter.

Lance Y. Hunter is an Associate Professor of International Relations in the Department of Social Sciences and Master of Arts in Intelligence and Security Studies program at Augusta University located in Augusta, GA, USA.

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From Liberation to Turmoil: Social Media and Democracy

Joshua a. tucker, yannis theocharis, margaret e. roberts, pablo barberá.

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Read the full essay here .

How can one technology—social media—simultaneously give rise to hopes for liberation in authoritarian regimes, be used for repression by these same regimes, and be harnessed by antisystem actors in democracy? We present a simple framework for reconciling these contradictory developments based on two propositions: 1) that social media give voice to those previously excluded from political discussion by traditional media, and 2) that although social media democratize access to information, the platforms themselves are neither inherently democratic nor nondemocratic, but represent a tool political actors can use for a variety of goals, including, paradoxically, illiberal goals.

About the Authors

Joshua A. Tucker   is professor of politics and a cofounder and codirector of the Social Media and Political Participation (SMaPP) laboratory at New York University .

View all work by Joshua A. Tucker

Yannis Theocharis   is a research fellow at the Mannheim Centre for European Social Research (MZES) .

View all work by Yannis Theocharis

Margaret E. Roberts is a professor in the Department of Political Science and the Halıcıoğlu Data Science Institute of the University of California, San Diego.

View all work by Margaret E. Roberts

Pablo Barberá   is assistant professor in the School of International Relations at the University of Southern California.

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Further Reading

Volume 30, Issue 1

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Volume 22, Issue 2

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SYSTEMATIC REVIEW article

Digital democracy: a systematic literature review.

• 1 Department of Public Administration, Faculty of Social and Political Sciences, Universitas Mahammadiyah Sinjai, Sinjai, Indonesia
• 2 Department of Accounting and Finance, Faculty of Economics and Business, Regional Campus of International Excellence “Campus Mare Nostrum”, University of Murcia, Murcia, Spain
• 3 Department of Government Studies, Faculty of Social and Political Sciences, University Muhammadiyah Yogyakarta, Yogyakarta, Indonesia
• 4 Department of Government Studies, Faculty of Social and Political Sciences, University Muhammadiyah Malang, Malang, Indonesia

Digital democracy provides a new space for community involvement in democratic life. This study aims to conduct a systematic literature review to uncover the trend of concepts in the study of digital democracy. This study used descriptive analysis with data sources derived from the Scopus database from the period between 2014 and 2020 (a total of 230 articles) and processed with VOSviewer. The results showed three dominant concepts, namely democracy, the internet, and movement. In addition, it was found that the digital era provides positive and negative impacts on democracy, that public knowledge in a quality digital democracy is important, and that there is strong elite control in virtual democracy. The results of this research can be used as a basis for developing digital democracy studies. Meanwhile, this study was limited by the fact that the articles reviewed were only sourced from Scopus and did not include publications from 2022. Therefore, future studies need to take a comparative analysis approach that uses the Web of Science (WoS) database and increases the time period in which articles are sourced.

1. Introduction

The advancement of technology, information, and telecommunications (ICT) has resulted in significant changes to practically every aspect of human life in the 21st century. Nowadays, virtualization and digitalization are comprehensively affecting the pattern of people's lives, in state, social, cultural, economic, political, and religious environments ( Blühdorn and Butzlaff, 2020 ). Particularly, in regard to democracy, there are many advancements or modern patterns caused by the rapid development of ICT. Communities and countries across the digital world can now communicate with each other very easily and accessing information is no longer a complicated process ( Bastien et al., 2020 ). Meanwhile, democracy in the old era used conventional patterns in which it was quite difficult for people to gain access to information related to government and state issues. Additionally, people found communicating or expressing opinions challenging. This was because of the complexity of the systems in democratic countries in the old era, which led to minimal public participation in activating democracy ( Dunan, 2020 ).

The development of the pattern of democracy through ICT advancement has brought about a great deal of change and provided many convenient benefits. Democracy in the digital era is able to provide easy access for the community, especially in terms of obtaining and expressing information ( Hardiman, 2018 ). However, as well as the positive impacts, digital democracy is also associated with negative impacts. The misuse of digital platforms as a means of community democracy is common. The key part of democracy in the digital era that all internet users must consider is their ethics and manners when expressing their thoughts ( Mahliana, 2019 ).

Digital democracy in its development is very much influenced by virtual space, especially social media. Meanwhile, social media is an embodiment of virtual space. The provision of internet access is the most important issue in this instance ( Indianto et al., 2021 ). The existence of virtual space and social media is one aspect that can provide great benefits for democratic life. Social media, if used by people as a means to activate democracy, will fulfill the true purpose of digital democracy ( Mahliana, 2019 ). The general population can readily obtain information and express their goals using social media platforms, such as Instagram, Whatsapp, Facebook, Twitter, TikTok, Line, blogs, websites, and other similar platforms. Moreover, nowadays, social media users are more likely to see content with varied meanings. This ease of access may undoubtedly be used to voice opinions, acquire information, and mobilize the populace on important topics in a democracy ( Waluyo, 2019 ). Lower-earning citizens can also take advantage of new technologies, such as social media (e.g., Facebook), which are extremely popular, inexpensive, and simple to use. In this instance, low-income individuals may demand increased information disclosure via these media, and local governments may use these tools to reach out to these citizens ( Guillamón et al., 2016 ). Additionally, candidates/politicians often use social media during political campaigns in which they use various platforms, such as Instagram, Facebook, and Twitter, to disseminate political programs and ideas that will be implemented.

When looking at the long journey of democratic methods in the past using conventional patterns and comparing them with digital democracy in the modern era, there is a fairly strong distinction. In the 1990s, internet information technology became known in the community. This had implications for how people adapted to democratic life ( Waluyo, 2018 ). In the past, people could only access information and express opinions through mass media, such as radio, television, and printed newspapers. Now, this behavior has shifted and people generally use digital platforms. Today, the public can promote democracy freely using the internet and social media ( Vittori, 2020 ). An additional benefit of the internet and social media is that they may be used to inspire and motivate. In the past, it was difficult to communicate directly with the government and society as they seemed so far away, but this has now been made possible by the internet and social media ( Hardiman, 2018 ).

Digital democracy is related to the use of digital media and networks for political and government purposes. In the context of democracy, digital technology greatly influences the democratic process through political mobilization, campaign strategies, and polarization of public opinion ( Gilardi, 2016 ). Furthermore, digital democracy is also related to the implementation of e-government ( Bastick, 2017 ; Sundberg, 2019 ; Filipova, 2020 ), e-Voting ( Yang et al., 2021 ; Lorenz-Spreen et al., 2022 ), and social movements ( Treré, 2015 ; Canella, 2017 ; Agur and Frisch, 2019 ; Pavan and Felicetti, 2019 ; Leong et al., 2020 ; Storer and Rodriguez, 2020 ).

Based on some of the explanations above, studies related to digital democracy are needed. The problems that arise in a digitally democratic society are things that must be minimized and normalized. Studies related to digital democracy in the world of democracy and science are fundamental and can provide implications or benefits for future democratic life. Therefore, researchers consider this to be an important issue and are interested in studying and analyzing how digital democracy is discussed and how it should be implemented. In studies related to democracy in the digital era, researchers try to use a structured literature review system when this method is considered to be capable of answering the researcher's basic questions and presenting relevant conclusions.

Several studies have demonstrated the development of digital democracy across the world. Bessant (2014) points out that digital democracy has succeeded in driving political change in Arab countries through the Arab Spring due to the involvement of students, who were able to use social media as a means of communication in developing resistance movements. Wells (2014) states that social media encourages the rise of civil politics because people are more concerned with political issues. Vlachokyriakos et al. (2014) show that the presence of e-voting succeeded in making the election process more efficient and effective. Lee et al. (2014) demonstrated that social media succeeded in breaking the chain of political inequality in Thailand, where young people were more active, especially in the case of the referendum. Natale and Ballatore (2014) highlighted the role of new media, specifically websites, in spreading influence and campaigning during the growth of the Five Star Movement (M5S) in Italy. Bessant and Watts (2017) show that Aboriginal tribes in Australia, as indigenous people, have succeeded in increasing their equality and political influence using social media. Vaccari and Valeriani (2018) argue that people's political participation via social media is greater in established democracies, such as Denmark, France, the United Kingdom, and the United States than in “third wave” democracies, such as Greece, Poland, and Spain. Michaesel (2018) looks at how the Iranian government strictly controls the internet through censorship of information to prevent the emergence of a democracy promotion movement.

Evans (2019) shows a strong correlation between massive internet use and the development of democracy in Africa. This study shows that democracy in African countries is currently heading in a new direction due to the strengthening of digital politics in the community to oversee the running of government and protest. Chitanana and Mutsvairo (2019) show how social media has succeeded in growing a repressive community resistance movement in Zimbabwe; people are using social media for citizen journalism and fighting for democracy. In Russia, Glazunova (2020) shows how YouTube has succeeded in becoming an alternative media used by Alexey Navalny as an opposition figure to organize mass protests in Russia, especially in the anti-corruption protest event in 2017. Finally, Flew and Iosifidis (2020) emphasize how populism is an aspect of the right wing that exploits the spirit of nationalism and has become stronger lately because it maximizes the use of social media. Another study analyzes the determinants of public engagement on municipal Facebook pages ( Metallo et al., 2020 ). The sample included 170 cities in Italy and Spain that used Facebook in 2014. The data indicate that excessive publication of city information on Facebook Pages has little effect on citizen involvement. Additionally, routinely posting information does not constitute public participation. However, if it is posted and made publicly available (for example, on a holiday), the possibility of public engagement increases. Additionally, citizen engagement on the city's Facebook page is dependent on the person's income level, with a negative correlation between income and participation. In comparison to these studies, which were conducted explicitly, this research makes a novel addition by using the systematic literature review (SLR) approach to demonstrate the trend of digital democracy studies and their analysis to make them more comprehensive and comparative.

2. Literature review

2.1. democracy in the digital era.

The development of globalization has many implications for present society. The current rapid globalization has minimized limitations in the global community. This is based on the rapid development of technology and information so that it is easy for the global community to access information ( Kud, 2021 ). Changes in the democratic patterns of society and government in each country have coincided with this massive development of information technology and globalization. Advances in technology, information, and communication have changed the democratic patterns of society and government so that they can move in digital spaces ( Blühdorn and Butzlaff, 2020 ).

There are positive and negative sides to the study of democracy in the digital era. The positive aspects make it easier for people to express their aspirations, form groups, protest policies, control policies put forward by governments, and so on. The point is, from this perspective, democratic countries are becoming more democratic because virtual spaces and internet access can provide opportunities for users to express their opinions ( Dwifatma, 2021 ). However, in this case, good understanding and ethics are needed so that people do not use freedom of expression to violate ethics in the virtual world as well as human rights ( Nasution, 2020 ). On the other hand, there is a negative side to democracy in the digital era. Public understanding of social media is something that is often a problem. Many cases of ethical violations and use are out of the realm of the public in the virtual world. These cases can be in the form of hoaxes, hate speech, defamation, and so on ( Masduki, 2021 ). The basic understanding of society in conveying and using freedom of expression on digital platforms is sometimes far beyond limits. This is one of the problems and challenges for democracy in today's digital era.

Based on the explanation above, digital democracy has a significant impact on society and government. Digital democracy can support the realization of democratization in a country. This can happen because the digital world makes it easier for people to control and express their aspirations regarding existing problems ( Charnock et al., 2021 ). On the other hand, the government as a policymaker should also provide substantial and periodic socialization, as well as education regarding how to use digital platforms properly ( Blühdorn and Butzlaff, 2020 ).

2.2. Virtual space and social media

Virtual space is a space that results from a simulation of reality and then becomes a hyperreality or the adoption of reality on a digital platform. Virtual space can also be interpreted as a form of virtual communication. Virtual space is present as an alternative solution for meeting human needs to socialize widely beyond limits. Meanwhile, social media is an embodiment of the virtual space. Internet access is the most important factor in this instance ( Indianto et al., 2021 ). The existence of virtual space and social media is one aspect that can provide extraordinary benefits for democratic life. The meaning or value of democracy can be achieved through social media, which make it easier for people to actively participate in a democratic country ( Mahliana, 2019 ). People can readily obtain information and express their goals using social media networks, such as Facebook, Instagram, WhatsApp, Twitter, Line, blogs, and websites, among others. This ease of access can certainly be used as a means of expressing opinions and gathering and mobilizing the masses regarding certain issues in a democratic country ( Waluyo, 2019 ).

To support this, substantial and periodic virtual political education is needed to support democratization in today's digital era. This is an important aspect for supporting the basic understanding of the community regarding how to use various digital platforms to support democracy ( Malik et al., 2020 ). There will be complex problems if the virtual political understanding of society is not fully fulfilled. Hoaxes, hate speech, defamation, discrimination, political stereotypes, and so on are things that can arise if the social media user community is not able to use social media properly.

3. Research method

This study examines various articles that are closely associated with digital democracy. Articles of an international scale and reputation are the main sources of reference in the preparation of this study. The focus of the review discussed in this study is based on several basic factors, especially in terms of understanding the concepts, impacts, and patterns related to digital democracy. Researchers are attempting to summarize studies that have been reviewed by previous researchers to find a common thread to understand how digital democracy takes place in the current era.

Figure 1 shows that this research began with a search for articles using the keyword “digital democracy” in the Scopus database for the 2014–2021 period. This search identified 258 articles that were then reviewed based on stages: a search for articles, import articles in the application software, and mapping of discussion topics.

Figure 1 . Flow diagram showing the different stages of the method used in this review with PRISMA.

Several articles that had strong links were obtained by researchers based on the following procedure: first, article identification attempted to sort and select various articles so that only those related to the topic were used. This was carried out by inputting the keywords “digital democracy” in the search column, with restrictions from 2014 to 2021. Based on the search process, 2,508 articles related to the topic were obtained. The second stage involved verifying the various articles found to determine whether they were really needed and were closely related to democracy issues in the digital era. Verification was carried out by limiting the subject area (social sciences), document type (article), publication stage (final), and language (English). The verification process identified 258 articles/journals that were relevant to digital democracy. These articles were used as a reference for studying “digital democracy”.

4. Data analysis

4.1. publication and leading author.

Articles on the topic of digital democracy are one of the the most popular types of study and continue to increase every year. Figure 2 shows that from 2014 to 2021, in general, there was an increase even though there was a stagnation in 2016. Furthermore, the year in which the highest number of articles were published was 2021 (89 articles). By contrast, the year in which the fewest articles were published was 2014 (14 articles).

Figure 2 . Number of publications from 2014 to 2021.

Furthermore, the 10 authors with the highest number of publications related to digital democracy between 2014 and 2021 are shown in Figure 3 . De Blasio had the highest number of publications (four articles). Furthermore, three authors, Casserro Ripolles, Sorice, and Trere, published three articles. Finally, six authors, Vaccari, Assenbaum, Ballatore, Berg, Condy, and Davies, published two articles.

Figure 3 . Top 10 authors of publications related to digital democracy between 2014 and 2021.

4.2. Correlation and grouping of themes in digital democracy studies

The following description is a follow-up procedure sourced from various articles/journals after the previous selection and verification process. The results of the review were processed using the VOSviewer application to categorize concepts based on groups. Figure 4 shows the various concept names displayed with cluster densities, with a total link of 511 and a total link strength of 821. The difference between cluster colors is an indication of differentiation between one discussion group and another focus group. This makes it easy for researchers to map groups of data so that they can be studied and analyzed. Regarding the study of digital democracy, Figure 4 displays different colors for each existing cluster and refers to the grouping of their respective concepts.

Figure 4 . Clusters of discussion topics related to digital democracy.

Figure 4 shows how the themes were grouped, and these groups were sorted for review with those that actually have a correlation based on the themes discussed. Table 1 maps concepts or themes based on clusters related to the study of digital democracy.

Table 1 . Themes grouped based on clusters.

Table 1 shows that cluster 1 predominantly discusses how the internet or digital space can be used as a forum to participate in strengthening democracy. In cluster 1, the most dominant keyword is internet. This shows that the topic of the internet has the highest frequency, or is often mentioned, in cluster 1. This happens because all the concepts written by the author always refer to the internet.

Gauja (2021) , for example, explains that the presence of digital networks can strengthen democracy as people can participate online to strengthen and activate it. Nowadays, public opinion can be channeled through digital platforms or social media. Twitter, Facebook, websites, and various other platforms can be used to communicate public opinion in a virtual form. Digital democracy, or what can also be referred to as e-democracy, on the other hand can function as campaign media. The breadth of access and the number of internet users are the main reasons why online participation is massive ( Flew and Iosifidis, 2020 ).

A fairly monolithic scientific argument is also elaborated by Smith and Martín (2021) . This study, conducted in Madrid and Barcelona in Spain, reveals that digital or technopolitical platforms can influence democratic activity and democratize a region or country. Smith and Martín (2021) also explain that digital features have become a platform for aspirations of community involvement and activism. This underlies the notion that the pattern of digital democracy must be strengthened through socialization and strong education so that people can understand the pattern of democracy in the virtual space. Additionally, Vittori (2020) further reveals that the community can influence policymakers through the digital space, where the masses can be mobilized virtually to provide reflection so that policies made by the government or members of parliament can be influenced. Thus, the digital space is highly beneficial for activating democracy. Democracy is one thing that can be realized through the active participation of citizens, and the internet and social media can be a platform to manifest this participation ( Fuchs, 2021 ).

Cluster 2 predominantly features the function of the community to control government policies and is also related to public understanding during political arguments in digital media. In cluster 2, the most dominant keyword is citizenship as all the concepts written by the authors always refer to the topic of citizenship because citizens participate in politics, primarily to control government policies; therefore, many authors research this topic.

To activate democracy and foster a participatory political culture, the public should massively control and oversee government policies. Democracy and participation is not only defined as using voting rights in general elections but also as guarding the elected political actors to keep the public interest first ( Masduki, 2021 ). Feldman (2020) finds that one of the most important things in digital democracy is a good basic knowledge of digital media users. Sometimes, there is a misunderstanding in society that freedom of expression in digital media is defined as a very high level of freedom. This is biased and out of control and leads to the violation of the human rights of individuals or political actors, hoaxes, SARA, black campaigns, and so on. Therefore, it is necessary for the public to have a strong awareness about how to argue when using digital media. Understanding which words to use and which arguments to engage with exemplifies this and underpins the appropriate manner in which to express opinions or argue in the digital world ( Moya, 2020 ).

Similar to the dominant concept in the previous cluster, cluster 3 predominantly features community participation in enlivening democracy. Therefore, the dominant keyword is participant, which means all authors refer to it in cluster 3. Even though every democratic country has its own representative council, the advancement of ICT allows people to directly control policy and debate freely through digital media ( Dommett et al., 2021 ). In terms of the implications or problems that arise because of regulations that deviate from government, the public can use social media to raise cases and mobilize the masses to oppose government regulations. This is what is referred to as public participation in the new era of digital democracy ( Siagian and Yuliarti, 2021 ). In the conventional era, people had to report to the government at the closest level and to representatives; however, in the era of digital democracy, people can express their opinions in digital spaces or platforms. The expression of public dissatisfaction on social media has led to governments improving policies or redelivering policy intentions. This is certainly very democratic, with the benefits of digital media positively impacting democracy ( Attatfa et al., 2020 ).

Cluster 4 predominantly discusses the impact of the presence of the internet and digital media on democracy, which has an impact on the ease and equality of public access to participation. Therefore, the dominant keyword is access, which means all authors refer to it in cluster 4. Bastien et al. (2020) explain that the ease of access offered by the digital space can be of great benefit to marginalized and disabled people. For example, social media can be used as a forum for channeling the opinions of this group of people. Social media that does not prioritize social stratification provides a positive space for this group. A democratic system that requires any citizen to have an opinion through social media can indirectly be properly accommodated. Social media is an alternative way for people to participate in and activate democracy ( Vittori, 2020 ). Finally, Dunan (2020) also suggests that democracy in the digital era makes people closer to the state and government. This is because of the lack of boundaries in the digital world, which allows people to easily convey their aspirations to the government. The community in this case can move away from the political culture of the subject and participate politically. In general, democracy in the digital era, putting aside its negative impacts, can provide great benefits for the community so that they can actively participate in a democratic state system.

The dominant themes or concepts featured in cluster 5 are capitalism and digital democracy. Marenco (2021) explains that digital democracy has a strong causality with the capitalist system. The dominant keyword is capitalism, which is referred to by all authors. The focus of cluster 5 is to link capitalism with political democracy; in a capitalist system, political democracy must be carried out. The concentration of economic and political power in a handful of groups indicates a pattern of digital democracy mobilization. In this instance, democracy in the digital era faces challenges. Capitalist groups can control and supervise internet users. This is a real problem for democracy in the digital age. To minimize this, digital media users are required to have knowledge about verifying the information contained in various digital platforms ( De Blasio and Viviani, 2020 ).

Finally, in cluster 6, the concept predominantly discusses the presence of digital media as an alternative to society in democracy. This is indicated by the fact that alternative digital medium is the dominant keyword, which means it is the main reference for authors in cluster 6. Democracy in the digital era requires citizens to have accounts on various social media platforms. These accounts can be used to as a weapon to convey opposing arguments against the government as a policymaker ( Gao et al., 2021 ). Additionally, digital media are now used as a tool for political advertising by political groups and individuals. In this instance, these advertisements have positive and negative values. This requires the public to be observant so that they can understand information in advertisements delivered on digital platforms ( Gauja, 2021 ). To support public understanding of democracy in digital media, the government should also massively provide socialization and education regarding how digital media should be used as a means of channeling aspirations. This is considered important for democracy in today's digital era. Positives and negatives are always present in democracy in the digital era; therefore, it is important to understand how to properly express opinions on social media or the internet ( Gauja, 2021 ).

4.3. The dominant themes in the study of digital democracy

Based on the data analysis undertaken, there are several dominant themes or themes that have a strong association with the study of digital democracy. This categorization or grouping of dominant themes aims to make the study more focused so that it can present a relevant conclusion. Additionally, the categorization and classification of dominant themes are also used because they can make it easier for the author to map out any topics that have a strong association with the topics discussed. Reviewing the studies of democracy requires verification or sorting of the data so that it is truly in line with the topic of a study. This is carried out so that the discussion or subject of the study is not too general and widespread. Figure 5 shows some of the dominant concepts associated with the study of digital democracy.

Figure 5 . Dominant topics in the study of digital democracy.

Looking at the group of words featured in Figure 5 , it would appear that of the various previous discussions on digital democracy, several groups discussed the dominant themes or concepts that tended to be discussed the most. Researchers in this study used an analytical tool called VOSviewer to process data and come up with dominant themes or concepts related to the study of digital democracy. The dominant concepts/themes that were often discussed by previous researchers included democracy, internet, movement, concept, public sphere, control, implication, framework, representative democracy, democratization, relationship, knowledge, participatory civic, citizenship, public opinion, media control, e-democracy, and online participation.

The color thickness in Figure 5 indicates how dominant each focus group is. The group of themes with the thickest colors were discussed the most. These various groups of dominant concepts have a strong mutualism symbiosis that makes it easier for researchers to come to conclusions that are truly conical to studies related to democracy in the digital era. This review of the dominant theme was needed to provide a reference for concepts that were often discussed. Therefore, the results of the processed data are shown in Figure 5 .

Based on the dominant concepts or themes related to digital democracy, as described in Figure 5 , several topics are quite dominant or have been frequently studied. The first dominant topic, democracy, is at the center of studies related to digital democracy. Democracy in the digital era is one of the topics discussed in the modern era. The presence of digital media has strong implications for democratic life. The positives and negatives presented by democracy in the digital era are complex and interesting issues to study. This foundation is one of the reasons why “democracy” has become the dominant discussion in various previous studies. Another dominant theme indicated by color thickness in Figure 5 is the internet. Democracy and the internet are groups that have strong causality in studies related to digital democracy. The presence of the internet raises the spirit of democracy in the community because of the convenience offered in the various virtual spaces in it. The internet arrived and changed people's democratic habits. However, there are many problems associated with the digitalization of democracy. These problems have been predominantly studied by several researchers.

Another dominant theme in the study of digital democracy is knowledge. The active participation of the community in the era of digital democracy must be accompanied by strong knowledge regarding the use of digital media in democracy. This is important to discuss because there are many cases of violations and irregularities when opinions are expressed on digital platforms ( Reiter and Matthes, 2021 ). Then there is the dominant theme of public opinion in the era of e-democracy. E-democracy, in this sense, is intended as a pattern of delivering public opinion through digital systems. In the modern era, people can more easily and freely express their opinions, conduct campaigns, and mobilize the masses ( Flew and Iosifidis, 2020 ). The presence of the internet has improved democracy. Although there are many drawbacks with virtual democracy, the minimal limitations associated with digital platforms are positive for society in terms of activating democracy and presenting democratic values ( Gauja, 2021 ).

There were other dominant themes that could not be fully covered by this study. Nevertheless, each dominant theme contained in Figure 5 has a correlation with one another and can be used as a reference for studies related to digital democracy. When conducting studies related to digital democracy, it is necessary to first understand the dominant concepts that have been discussed by previous studies. This is important because it can make it easier for researchers to summarize and produce relevant conclusions regarding the theme of digital democracy.

4.4. Period of article publication in digital democracy studies

The next elaboration relates to the period of publication of articles in the study of digital democracy.

Figure 6 shows articles published during the period from 2014 to 2020. When examined based on thickness or color dominance, studies related to digital democracy published between 2014 and 2016 were more dominant in discussing the internet, participants, services, and so on. This means that during the 2014–2016 period, focused or dominant studies discussed how the internet can be used as a field for community participation in democracy. From 2016 to 2018, the study that was dominant began to change and attempted to examine the benefits of the internet for presenting democracy in democratic countries. The studies in this time span were also dominant in discussing the internet as a means of control and conveying aspirations and as a space for movements that can support democracy. Then, the period from 2018 to 2020 saw the emergence of capitalism, digital advertising, and virtual space controlled by certain groups. This means that there has been a very dynamic study of digital democracy. However, in general, studies on related themes are always dominantly related to, or have implications for, “democracy”.

Figure 6 . Publication trend of the study of digital democracy.

Studies and publication of articles on digital democracy are considered very important given the massive changes that have occurred in the modern era. Additionally, democracy in the digital era has challenges and shortcomings associated with its implementation; therefore, future studies need to be scaled up to provide updates and communicate the lessons learned about digital democracy. The novelty presented in studies related to digital democracy provides benefits as a reference and alternative solution for the future. Therefore, researchers expect to undertake large-scale studies and present new findings to provide lessons that can be incorporated into future studies related to digital democracy. One of the important studies conducted by De Blasio and Viviani (2020) , “Platform party between digital activism and hyper-leadership: the reshaping of the public sphere”, emphasizes that politicians/political parties can maximize social media to repair their damaged image in the eyes of the public through smart and sustainable political advertising. In addition, politicians/political parties must improve their intensive communication skills through digital means (social media) to connect them with the public so that their damaged reputation can be repaired.

4.5. Co-authorship analysis

Network mapping by author's name was also carried out in this study. The involvement of the authors in relevant studies is important because it can show the intensity of the author and the relationship between authors in this area of study. Network mapping by author can also show how active an author is in collaborating with other researchers and can also find references between authors to indicate who might collaborate with each other in the future.

As shown in Figure 7 , the author with the most publications was De Blasio (four articles). However, when a co-authorship analysis of collaboration between authors was undertaken, of the 43 selected authors with at least two articles, only one cluster with four authors (Reinhard, Knufher, Heft, and Meyerhofer) was identified and indicated a very minimal collaboration in the topic of digital democracy. De Blasio was not among those who collaborated with other authors.

Figure 7 . Co-authorship analysis.

5. Conclusion

Studies related to digital democracy are important and need to be widely presented. The rapid development of ICT has brought change and dynamism to the pattern of democracy in the digital era. This research reveals several dominant studies related to digital democracy. Some of the most important aspects of digital democracy were as follows: first, the digital era and its benefits for democracy—the presence of the internet has many implications for the pattern of democracy. The internet, which offers freedom and easy access for users, can be used as a forum for community participation to actively contribute to democracy. Virtual space provides a new dignity to the rise of democracy, thus democratic values can be presented in today's digital era. A second aspect involves people's knowledge of democracy in the digital era. In the era of digital democracy, freedom of expression is not regarded as a completely unlimited freedom. Values and ethics need to be applied when expressing opinions in virtual/digital spaces. Therefore, public knowledge is fundamental in the era of digital democracy (e-democracy). Finally, another important aspect is the presence of capitalism and control in democracy. In today's studies of digital democracy, there are indications of control by a group of elites in the virtual democratic pattern of society. This negativity affects democracy in the digital era, but the basic understanding of society is one of the main shields against this problem.

This research is useful for showing the development of, and urgent need for, digital democracy at a global level. However, this research also has limitations. First, the articles reviewed were only sourced from the Scopus database; therefore, there are no comparison data. Second, it excludes articles published in 2022, during which time the COVID-19 pandemic endured and even worsened in several places, which of course greatly affected virtual democracy. Therefore, further studies need to apply a comparative analysis approach that uses the Web of Science (WoS) database as a source of highly reputable international journals and widen the time period from which to source published research.

Data availability statement

The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.

Author contributions

All authors listed have made a substantial, direct, and intellectual contribution to the work and approved it for publication.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher's note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

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Keywords: digital democracy, government, participation, social media, internet, movement

Citation: Congge U, Guillamón M-D, Nurmandi A, Salahudin and Sihidi IT (2023) Digital democracy: A systematic literature review. Front. Polit. Sci. 5:972802. doi: 10.3389/fpos.2023.972802

Received: 19 June 2022; Accepted: 09 January 2023; Published: 09 February 2023.

Reviewed by:

Copyright © 2023 Congge, Guillamón, Nurmandi, Salahudin and Sihidi. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

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Social Media Effects: Hijacking Democracy and Civility in Civic Engagement

Bolane olaniran.

6 Texas Tech University, Lubbock, TX USA

Indi Williams

7 Arizona State University, Tempe, AZ USA

Perceived as an equalizing force for disenfranchised individuals without a voice, the importance of social networks as agents of change cannot be ignored. However, in some societies, social networks have evolved into a platform for fake news and propaganda, empowering disruptive voices, ideologies, and messages. Social networks such as Twitter, Facebook, and Google hold the potential to alter civic engagement, thus essentially hijacking democracy, by influencing individuals toward a particular way of thinking.

Computer-mediated communication , a novel and emerging area just a few decades ago, has evolved from an academic collaboration tool to what is commonly referred to as new and social media. New and social media have been touted as an equalizer for disenfranchised individuals to participate or contribute in civic engagement and to foster democratic ideals. However, the current state of social media and networking sites leave individuals to conclude that these media platforms may be holding democracy hostage instead of leading to the free and equal democratic ideals they were believed to support. Consequently, this chapter emphasizes that it is imperative to figure out a way to maintain sensible dialogues that promote democratic principles.

New and social media are hailed as vehicles for providing a voice to the voiceless. They are also viewed as a way to overcome state-controlled media and content (Bartlett, Birdwell, & Littler, 2011 ) especially in the developing world (Bartlett, Birdwell, & Littler, 2011 ). However, social media platforms are also increasingly being used as a means for empowering disruptive voices, messages, or ideologies (e.g., xenophobia, neo-Nazism, anti-immigration/ globalization, cultural homogeneity, etc.) (Cook, Waugh, Abdipanah, Hashemi, & Rahman, 2014 ; Gleason, 2013 ). The ability of a person or group to overstate an agenda and dominate the conversation is easily accomplished on social media such as Twitter. This is because social media do not subscribe to the same established journalistic rules of vetting and reporting news. Furthermore, the size of a group or an organization pushing a particular message no longer matters.

This chapter explores how social media have become a platform for fake news and propaganda to influence certain audiences toward a particular way of thinking. Consequently, social media outlets and people who consume information through them are putting pressure on the idea of democracy such that democratic societies as we know them may cease to exist. Along this line, this chapter explores how Twitter and Facebook in particular are used in a manner that creates chaos within regions and have arguably become an authoritative vehicle for persuasion (Cook et al., 2014 ; Waters & Williams, 2011 ). The capacity to morph or create multiplier effects suggest that social media messages, such as tweets and retweets of a few minority influencers, can become something considerably larger in terms of support of a person or a particular policy (Cook et al., 2014 ; Wilson, 2011 ). Therefore, this chapter incorporates specific examples and analogies from events such as the Brexit vote and US elections, along with subsequent tweets by the president of the United States.

New and Social Media

New and Social media provide information for individuals in certain networks while they also create multiplier effects as those same individuals attempt to reach others in their networks. Multiplier effects such as these that occur through social media can go on in perpetuity. For instance, it was noted that in 2010 that individuals between 8 and 18 years of age were exposed to a daily average of 10.45 hours of various media technology (see Dahl & Newkirk, 2010 ). However, recently people have been exposed and engaged in what has been termed as a mass-self communication (Castells, 2013 ) that is embedded within ubiquitous computing (Moffitt, 2016 ). Ubiquitous computing is also known as the third wave of computers, in which hand-held devices with Internet wireless technology are widespread and highly accessible. In essence, this dynamic constitutes social media that readily put information and messages in the hands of individuals at a speed never seen before. Therefore, the result is an evolution of electronic communication technology (Castells, 2013 ). This evolution is stimulating new patterns of production, reception, content, and circulation, allowing for new forms of engagement through participation, production, and consumption. Consequently, communication is no longer confined by geographical boundaries, but rather globalized to the extent that it is linked to the “ideology of worldwide communication” (Mattelart, 2002 , p. 591). In other words, social media enable power where an online community or the virtual world has become a dialectical space. It is within this space that people can initiate or perform roles as producers of content, broadcasters, audiences, and political actors (Castells, 2013 ).

Social Media and Political Discourse

The political landscape has been transformed by new and social media. This transformation has resulted in an increased rise of populism around the world. Subsequently, the active role of the audience as made possible by social media has become a great opportunity for populist actors to spread their political messages or agendas (Moffitt, 2016 ). The proliferation of populism through media is not new. Historically in Europe, the populist radical-right parties (PRRPs) and actors have been using media (e.g., TV, radio, print press) as platforms for their messages since World War II (Mudde, 2013 ). However, new and social media reache a larger audience with political content via Facebook, Twitter, YouTube, or Weibo (Moffitt, 2016 ). This audience can now be reached at greater speeds and within a short time span (DeLuca, Lawson, & Sun, 2013 ).

The role of new and social media is central to the populism movement because it represents political strategies in novel and exciting forms (Mudde, 2007 , 2013 ; Moffitt, 2016 ). In this vein, social networks are better suited as a method of creating social webs designed to facilitate the diffusion of desired behavior among groups of people (i.e., Centola & Christakis, 2014 ). However, the nature of social media in a political discourse must be conceptualized within the context of democracy theory. For the most part,  democratic theory subscribes to the idea of human involvement in non-activist decision making, otherwise referred to as participatory democracy (Moote, McClaran, & Chickering, 1997 ). At the core of participatory democracy theory is the role of the public or citizens in rational evaluations of the pros and cons of an issue. This is especially the case when individuals are participating in decision making or offering rewards (Kweit & Kweit, 1987 ; Moote et al., 1997 ). However, with the introduction of social media, affected people are encouraged to voice their opinions even though they do not necessarily engage in the democratic process. More specifically, the coherent discussion of ideas has been substituted with the spread of fragmented ideas, resulting in the spread of populism (Wirth et al., 2016 ). To this end, social media in political discourse are rife with a pathological form of democracy (Betz, 1994 ; Engesser, Ernst, Esser, & Büchel, 2017 ). Similarly, although the spread of populism extends beyond Westernized societies, Mudde ( 2007 ) concurs that populism has become mainstream in Western democratic politics.

Social Media Impact on Voting Turnout

Bond et al. ( 2012 ) found that online political mobilization messages distributed via individual self-expressions and shared through personal social networks (i.e., Facebook or Twitter) lead to self-guided information seeking and, perhaps, self-serving behavior. Consequently, these messages subsequently impact voting turnout behavior. Indeed, the study indicates the powerful effect of online political mobilization. Furthermore, the authors conducted a randomized controlled trial with all users who accessed the Facebook website on 2 November 2010, the day of the US congressional elections. Users were then randomly assigned to a “social message” group ( N  = 60,055,176), an “informational message” group ( N =  611,044), or a control group ( N  = 613,096). The findings suggest that when political mobilizing messages are disseminated by close friends in a given personal social network, the influence is four times more on the total number of validated voters mobilized compared to the informational message group and control group. In other words, social networks have been and continue to be used to impact individuals’ voting turnout behavior (i.e., Kramer, Guillory, & Hancock, 2014 ). Hence, sharing messages in social networks impacts an individual’s emotions, which ultimately results in actual real-world actions. This finding serves to rule out any naïve understanding of social networks as a mere way of contacting “old friends” and family members or in positioning commercial brands.

Populism the Symbolic Frontier

Although there exists haphazard scholarly analysis of populism as an ideology, strategy, discourse, or political logic, Moffitt ( 2016 ) asserts that the best way to conceptualize it is as a political strategy. This strategy entails “the repertoires of embodied, symbolically mediated performance made to audiences that are used to create and navigate the fields of power that comprise the political, stretching from the domain of government to everyday life” (Moffitt, 2016 , p. 38). Furthermore, within this dynamic, societies are politically polarized in two homogeneous and antagonistic groups: “the pure people” versus “the corrupt elite”, “Us” versus “They”, or “citizens” versus “immigrants”. The official political performance reflects people’s general will to forcefully reflect their sovereignty. Consequently, the way in which these groups are formed is through the unsatisfied demand as the minimal unit of political social analysis. This unsatisfied demand, along with other unsatisfied needs, becomes a springboard for people to identify a common antagonist/enemy believed to be the perpetrator even if this entails the use of fake news.

Therefore, the more people can dissociate themselves from the technocratic style of “politics as usual”, the better their appeal (Disch, 2012 ; Saward, 2010 ; Severs, 2010 ). For example, President Donald Trump said during his 2016 presidential campaign that he likes poor and uneducated Americans more than the rich. Subsequently, this situation evinces populist leaders’ performance based on pretending to be “outsiders” in mainstream politics to give perceived distance between their actual experiences as the “elite”. Therefore, populism creates a symbolic frontier among social groups in a way that hegemony is reinvented as a government of the people’s will (Wirth et al., 2016 ). One way this occurs is through the acceptance of a leader who fosters anti-immigrant discourse in the EU and the US, two nation-states where immigrants are treated as outsiders.

Polarized Political Groups Influencing Human Behavior

The use of social media platforms allows people to share messages with a larger audience in a way that was not previously possible. All this sharing can now be accomplished without running the risk of censorship, a common barrier of traditional media outlets. On social media there are active communities (e.g., right wing, racist, neo-Nazi) that seek to disseminate hate messages to their members and distribute propaganda to recruit new membership. These groups rely on platforms such as Twitter, Facebook and YouTube to communicate (O’Callaghan et al., 2013 ). Consequently, messages sent via social media will continue to spread through followers to others. Reciprocation of messages occurs in the same manner.

Perhaps a significant contribution of social media to any ideological or political movement, such as populism, lies in the fact that it helps to influence users’ behavior. An attempt to influence behavior must not only focus on the informational effect, but also on the effect the message will have on the recipients. Additionally, it must increase the likelihood of the various behaviors the message will spur as it transmits from person to person through the social network. This variation is based upon online mobilization as messages spread through strong-tie networks existing offline and in online arenas (Bond et al., 2012 ).

Some research has shown that the organization of community groups online is decentralized, while other research has found that some groups exhibit a more centralized disposition (Chau & Xu, 2007 ; O’Callaghan et al., 2013 ). Nevertheless their construction, the purpose of using new media to further any ideology is to mobilize groups. This includes, but is not limited to, furthing the populist movment. This mobilization was found to be the case in more extremist groups investigated in a conservative movement in the US (Blee & Creasap, 2010 ; Bond et al., 2012 ). Additionally, Bond et al. ( 2012 ) reported that online messages influenced political self-expression and information seeking, along with individual voting behavior. Moreover, online messages influenced not only those who received the messages, but also their friends and friends of friends (Bond et al., 2012 ). This was especially true when there was a strong tie or close friend relationship between individuals.

The Impacts of Social Media in Political Elections

The story of the last two US presidential campaigns focuses on the use of social media. However, each candidate used social media for different reasons and in order to accomplish different goals. The 2008 election focused on disseminating campaign-relevant information based on facts, while the 2016 election focused on propaganda through the deployment of fake news and bots. The research indicated that the election of President Obama brought about an increase in the surge of the white nationalist movement. Specifically, the study showed that the day after Obama was elected president occurred the biggest single increase in membership of Stormfront (a White nationalist organization) and that Trump rode the wave to become president in 2016 (Hinck, 2018 ; Stephens-Davidowitz & Pinker, 2017 ). Using social media as his persuasive tool, Trump’s campaign imbued anger and hyper- partisanship by advocating policies or messages that called for isolation from the world and the closing of the border to establish an immigration policy.

According to Persily ( 2017 ), social media were used in a way to upset established paradigms on how to run and win elections to the extent that President Trump’s campaign broke established norms of politics. However, President Trump and the 2016 election is not the only occurance of populist nationalism that appears to thrive on social media. Other examples include the rise of the Five Star Movement in Italy, the pirate party in Iceland, and the keyboard army of President Duterte in the Philippines. Furthermore, in Europe the successful Brexit referendum revealed that supporters were seven times more active than their opponents on Twitter and five times more active on Instagram (Persily, 2017 ).

Fanaticism and Viral Nature of Social Media

It is important to understand what make social media so powerful as a communication tool. The legacy of traditional media as gatekeepers or campaign mediators is declining in terms of influence and power, with no alternative institutions to fill the void. More importantly, President Trump taped into this void by excessively using social media and Twitter. It was noted that from August 2015 to election day there were more than a billion tweets regarding the presidential election ( Twitter.com , 2016 ; Persily, 2017 ). Furthermore, Trump’s followers on the platform outnumbered Clinton’s followers by 33% (CBSNews, 2016 ). Subsequently, every tweet from Trump or his allies was further retweeted by his loyal followers and supporters. Particularly, it was found that in mid-2016, Trump’s tweets were retweeted three times as much as Clinton’s, while Trump’s Facebook post were re-shared five times more ( Journalism.org , 2016 ; Persily, 2017 ). Persily ( 2017 ) also discovered that despite much lower advertising budgets or spending overall, the Trump campaign spent more on Facebook than the Clinton campaign.

Perhaps the viral nature of information on social media gives it power. This may be because messages (e.g., political) in social networks influence users’ emotions, making social media messages effective tools of persuasion (Kramer et al., 2014 ). The ability to deliver both real and junk news (i.e., propaganda, misinformation) makes the media platform potent. Malicious activities such as harassment, hate speech, and spamming are just a few of the negative ways social media are being used (Howard, Bolsover, Kollanyi, Bradshaw, & Neudert, 2017 ). Bots on social media platforms can quickly send messages and replicate themselves in a way where the messages appear as if sent by a human being. Social media bots are automated accounts that are set up to act as if an actual person is using them. Bots are often used for propagating propaganda from both within and outside the country. Moreover, the notion of sock puppetry denotes that large followings via social media platforms can be easily gained for an insignificant price.

Therefore, social media provide dangerous ways of spreading junk news within social  networks comprised of friends and family. Prior research found that social media favor sensationalist content, regardless of whether the message was fact-checked or not (McCoy, 2016 ; Vicario et al., 2016 ). Notwithstanding, when misinformation is combined with automation such as bots, then social media become a tool for computational propaganda (Howard et al., 2017 ; Kümpel, Karnowski, & Keyling, 2015 ). Cambridge Analytica (part of Trump’s social media digital strategy) claimed that it targeted 13.5 million voters in 16 battleground states to discover hidden Trump supporters that polls had ignored. Also, Cambridge Analytica targeted Clinton supporters (e.g. white liberals, young women, and African Americans) with messages aimed to reduce turnout among those groups (Persily, 2017 ).

Political Polarization and Lack of Censorship

Social media offer a direct connection to people and thus allows for the spread of fragmented ideas such as populism to circumvent journalistic gatekeepers. In this way populists can present uncontested or unvetted ideas directly to their audience and articulate their ideology (Engesser et al., 2017 ). Hence, the rise of new media and political polarization creates a binary political strategy to increase political participation and voting turnout among individuals who see themselves as victims, or powerless, in the democratic process. Notwithstanding, the lack of control and censorship in new and social media has become a niche for extremist groups such as ISIS (Islamic State of Iraq and Syria) or neo-Nazis to spread their ideology. It is within this landscape that traditional media are forced to line up with polarized content in new media in order to keep their audience, while users are caught in the middle or forced to take a side. This dilemma, however, is the antithesis of the tenets of participative democracy (Moote et al., 1997 ). More importantly, traditional media are reinventing what is defined as news to the extent that they are actively mining social media for what they believe their audience wants to view.

The fact remains that social media platforms have become fertile ground for fake news and propaganda as evidenced in the 2016 US presidential election. BuzzFeed found that false election stories from hoax sites and hyper-partisan blogs generated more engagement than content from real news sites during the last three months of the election and post-election. Users shared false stories such as that Pope Francis endorsed Donald Trump and/or that Hillary Clinton sold weapons to ISIS. These stories and others were shared (e.g. retweeted) hundreds of thousands of times. More importantly, another report found that users were not interested in any news that disagreed or deviated from their accepted premises (PBS Newshour, 2016a ). Subsequently, people continued to actively seek and present false information as long as it supported their respective viewpoints.

Furthermore, any group can lend its Twitter support to a particular cause or person such that the control of an ideology or principle can gain an allegiance for a price (Ashton, 2013 ; Cook et al., 2014 ). Similarly, social media are increasingly being used by individuals who want to profit based on the number of clicks. In order to do this, they deliberately spread false and fake news to enrich themselves. Persily ( 2017 ) investigated the profit motive of social media users residing both inside and outside (i.e., Macedonia) the US. These users reported that publishing pro-Trump and anti-Clinton stories on about 140 websites dealing with US politics could earn them a fortune. One Trump supporter commented that he would have been willing to promote Ms. Clinton and smear Trump if the tactic was profitable. However, he discovered that similar Trump supporters were more fanatical and/or emotionally connected to their candidate than Clinton’s supporters (McCoy, 2016 ). Furthermore, he stated that  Trump supporters were more likely to believe anything when compared to Clinton’s supporters. This is because demographically Trump supporters are less educated, open to deep-seated beliefs, and willing to accept conspiracy theories as truth (Persily, 2017 ; Peters, 2017 ; Sides, Tesler, & Vavreck, 2017 ).

Social Media, Politics, and Propaganda

Twitter, for example, has increasingly been used in political elections of nation-states and in the spread of ideologies such as displayed in the Brexit movement and the 2016 US presidential election (PBS Newshour, 2016b ). Additionally, web-based botnets represent a significant number of Twitter traffic (Boshmaf, Muslukhov, Beznosov, & Ripeanu, 2011 ; Cook et al., 2014 ). To this end, propaganda and misinformation appear to be the norm in social media networks such as Twitter and Facebook. Social media bots (i.e., botnets, bots) are designed to manipulate the passage, transfer, and volume of the social narrative, which makes them ideal for the spread of homogeneity, as opposed to diversity, within their message. This inherent functionality is why bots are frequently used to spread beliefs (e.g., populism) and computational propaganda. Message distribution via botnets is popular due to the fanaticism of select users who demonstrate an insatiable desire to consume and redistribute information despite the source. Many of these messages carry divisive narratives that tend to transform civic engagement into dichotomies, pitting one group of people against another without allowing for consensus or compromise. Furthermore, fake news websites and bots attract traffic and drive engagement. Collectively, they aim to influence conversations and demobilize opposition through false support (Howard et al., 2017 ).

The size of a group or an organization does not necessarily have to reflect the level of influence delivered through social media. Twitter has been used in a manner that can create both stability and chaos within regions. Twitter has also arguably become an authoritative vehicle for persuasion (Cook et al., 2014 ; Waters & Williams, 2011 ). For instance, the Pizzagate conspiracy theory, where Michael Flynn Jr. (the son of fired National Security Agency director Michael Flynn) tweeted a false story about Hilary Clinton and her campaign manager being involved in a child sex ring. Unfortunately, the tweet led to a man who believed the theory entering the pizza parlor mentioned in the tweet with a rifle and firing shots before being arrested (Persily, 2017 ). Moreover, Twitter can greatly influence two-party dominated elections such as those in the US, UK and Australia, where prominence is sited on the support for one political leader over another (Cook et al., 2014 ). For example, it has been reported that tweets for Hilary Clinton and Donald Trump by party loyalists using sock puppetry or bots at one point stood at 20% and 33%, respectively (PBS Newshour, 2016b ). Similar results were found in the 2013 Australian federal elections, where large numbers of fake Twitter followers were found for both the incumbent prime minister and the leader of the opposition (Butt & Hounslow, 2013 ; Cook et al., 2014 ).

Prior to the Brexit vote, some of the messages tweeted to sway votes included “We are British not Europeans”, “Immigrants are terrorists”, and “Immigrants have taken away our jobs”. Additionally, Donald Trump’s 2016 campaign slogan “Make America Great Again”, was coupled with Twitter messages referring to Mexicans as rapists, Muslims as Islamic terrorists, and the North American Free Trade Agreement (NAFTA) agreement as the worst trade policy ever. In all cases, the opposition always touted the supporters of such ideologies as a basket of deplorables. Unfortunately however, these extreme viewpoints are now the norm, reality in a post-truth world. New scientific evidence attributes this not to the fact that politicians are more crooked than before, but rather that facts are futile. In other words, it is not that particular negative beliefs are more popular than positive beliefs, but that followers at times become more aggressive at distributing their views over other groups. More importantly, misinformation through social media, once limited to select viewers, has become shareable to all (Peters, 2017 ). For example, ideological extremism, misinformation, and the intention to persuade readers to respect or hate a candidate/policy based on emotional appeals through social media were reported in Michigan during periods leading to the 2016 USA presidential election. This fake news outperformed professional real news, substantiating the claim that truth is relative and based upon a particular political stance and/or belief system (Howard et al., 2017 ).

Implications

In social media, trending, tweeting, and retweeting are key metrics, even though the metrics can be manipulated, bought, or faked to create the impression that a particular issue represents the opinions of the majority. The reality though is that these messages are designed to appear as truth. Thus, political agendas such as populist ideologies, among others, can be manipulated as original or authentic when in fact this is not the case. Quite often, crazy ideas, lies, and conspiracy theories spread more rapidly than facts through social media. Subsequently, by the time information is fact-checked, the damage is already done and remains irreversible (Howard et al., 2017 ; McCoy, 2016 ; Persily, 2017 ; Peters, 2017 ). Therefore, it becomes difficult to engage in a democratic process where everyone can deliberate and consider all points of view. Moreover, the implication for the socio-cultural perspective may be greater especially when hatred, ethnocentrism, and separatism philosophies become the norm, as both the Brexit and the 2016 US elections indicate. The role that social media plays in hijacking democracy is clear in these elections, as the winners in both cases were the minority. For example, President Trump was elected based on the Electoral College vote, when in fact he lost the popular vote by 3 million votes.

User Anonymity and Authenticity

With social media, authenticity and trustworthiness of information, along with a sender’s identity, are hard to discern (Engesser et al., 2017 ). Furthermore, the anonymity facilitated in social media contributes to phony online personas that can be created by users or even botnets. According to PBS NewsHour ( 2016b ), bots can be purchased very cheaply (Ashton, 2013 ), and as a result, they become a critical tool to influence political movement and manipulate metrics. Furthermore, it is hard to verify messages that bots distribute versus messages from a real person. Also, bots contribute to fake tweets, since they are soley designed to sway opinions (i.e., slacktivism) (Cook et al., 2014 ; PBS NewsHour, 2016b ). The danger, however, is that given a significant number of demographics (i.e., millennials) get their news through social media platforms and often from friends, family members, and acquaintances (e.g., social media influencers), they are less likely to do due diligence in questioning the authenticity of messages via tweets, retweets or Facebook postings. The sheer number of followers of a particular message is likely to convince individuals of the need to subscribe to similar beliefs and ideologies being promulgated by a sender even when such ideas may be false or run contrary to an individual’s beliefs or values. This approach to information or message dissemination is contrary to what democracy theory of participation is proposed to accomplish in terms of not functioning or serving activism, as discussed previously. Specifically, the populists attack opponents or blame the elite for whatever problems they see in the democratic process (Engesser et al., 2017 ).

Message Volume

By Twitter’s own estimation in 2013 there were roughly 10.75 million non-genuine Twitter accounts (D’Yonfro, 2013 ) in the form of fake followers, along with accounts associated with individuals with numerous personas (Yarow, 2013 ). The number of messages posted on Facebook or tweeted over Twitter also makes it impossible to censor or discern real news from fake news (PBS, 2016a November). As a matter of fact, it was reported that fake news such as the claim that Pope Francis endorsed Donald Trump and that Hilary Clinton sold weapons to ISIS received a significant level of attention or engagement when compared to real news by the New York Times during the 2016 US presidential election (PBS Newshour, 2016a ). When a person uses multiple online personas constructed to look like an authentic identity (i.e., sock puppetry) (Cook et al., 2014 ), it begs the question of motive. The practice of sock puppetry has one underlying commonality, to self-promote a particular cause. The practice has been linked to online business promotions (Streitfield, 2012 ), political support (Cogburn & Espinoza-Vasquez, 2011 ), and terrorist coercion (Conway, 2012 ). In regard to terrorist coercion, for instance, the ISIS terrorist group has been linked to setting up thousands of fake Twitter accounts to recruit individuals (PBS Newshour, 2016a ).

As new and social media are here to stay, so is the idea of fake news or computational propaganda. The challenge, however, is that with social media it is hard to maintain a sensible and cordial dialogue, which is critical to democracy. One of the challenges with early social networks was that in some cases only like-minded individuals were creating and joining online communities. However, social media are now extending the reach of a few like-minded individuals in a way to shape policy for societies and nations as a whole. For now, populist ideology, the alt-right, alt-truth, and the rest are prevalent. What comes next no one knows. However, if the past is indicative of the present, the future is more likely to be far worse. Not only was the alt-right group able to endorse both President Trump and Brexit, but it has been able to shift public rhetoric from embracing diversity to a homogenous society where, for example, a country rooted in immigrants is closing doors on immigration, leading the way to an anti-immigrant stance. Events following the 2016 election (e.g., the Charlottesville, VA, riots) have intensified conflicts and set back race relations in the US. However, while the president had the opportunity to calm the public, he responded late, with a response that worsened the situation. Similar criticisms were given in regard to President Trump’s response to the COVID-19 pandemic large-scale outbreak accross the US. Another hot political issue surrounded the separation of children from parents who illegally cross the US border from Mexico. However, instead of finding a constructive solution, the current administration, along with President Trump, categorized the problem as simply enforcing the previous administration’s policy. This justification was given despite evidence that there was no such policy from either the Bush or the Obama administrations (Robertson, 2018 ). As a matter of fact, some states and former US attorney generals from both the Bush and Obama administrations have linked child-parent border separations to the current US attorney general’s (i.e., Jeff Session’s) announcement of a zero-tolerance policy in April 2018. The zero-tolerance policy has resulted in around 2000 children being separated from their parents within a six-week period (ALM Media, 2018 ). However, and despite the policy, the current whereabouts of these children remain unknown.

This chapter argues that it is imperative to figure out a way to maintain sensible dialogues that promote democratic principles. However, this must be done not just on Twitter or social media, but in society at large by bridging the gap between proponents and opponents of diverse political parties on certain political ideologies. However, in order for this to succeed, individual citizens will need to confront their own confirmation biases. All parties must demonstrate a willingness to seek opinions that extend beyond their individually held beliefs and ideologies (Rothwell, 2017 ). One way of doing this is to conscientiously seek disconfirming information about issues and policies, to engage people in constructive dialogue, and to listen to the views of individuals a policy might affect. This is especially true when it comes to individuals who may have different opinions, cultures, and/or perspectives. Otherwise, the principle or foundation upon which democracy exists via participatory democracy or inclusive participation as it is now known may cease to exist. This appears to be the case when social media facilitation of propaganda is coined as genuine and truthful information. At the same time, what counts as news and foundations for ethics in news (due to mass media mediation) is already under siege, as traditional news media have lost the battle concerning their roles as mediators of facts and gatekeepers of truth.

Contributor Information

John Jones, Email: [email protected] .

Michael Trice, Email: ude.tim@ecirtm .

Bolane Olaniran, Email: [email protected] .

Indi Williams, Email: [email protected] .

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• Social Media Seen as Mostly Good for Democracy Across Many Nations, But U.S. is a Major Outlier

Most think social media has made it easier to manipulate and divide people, but also say it informs and raises awareness

Table of contents.

• Most do not think they can influence politics in their country
• 2. Views of social media and its impacts on society
• Widespread smartphone ownership while very few do not own a mobile phone at all
• Most say they use social media sites
• Frequent posting about social or political issues on social media is uncommon
• Acknowledgments
• Appendix A: Classifying democracies
• Appendix B: Negative Impact of the Internet and Social Media Index
• Appendix C: Political categorization
• Classifying parties as populist
• Classifying parties as left, right or center
• Appendix E: Country-specific examples of smartphones
• Appendix F: Country-specific examples of social media sites
• Methodology

This Pew Research Center analysis focuses on technology use and views of internet and social media in the context of democracy and society. The survey was conducted in 19 advanced economies in North America, Europe, the Middle East and the Asia-Pacific region.

For non-U.S. data, this report draws on nationally representative surveys of 20,944 adults from Feb. 14 to June 3, 2022. All surveys were conducted over the phone with adults in Canada, Belgium, France, Germany, Greece, Italy, the Netherlands, Spain, Sweden, the United Kingdom, Japan, Malaysia, Singapore and South Korea. Surveys were conducted face to face in Hungary, Poland and Israel. In Australia, we used a probability-based online panel.

In the United States, we surveyed 3,581 U.S. adults from March 21 to 27, 2022. Everyone who took part in this survey is a member of the Center’s American Trends Panel (ATP), an online survey panel that is recruited through national, random sampling of residential addresses. This way nearly all U.S. adults have a chance of selection. The survey is weighted to be representative of the U.S. adult population by gender, race, ethnicity, partisan affiliation, education and other categories. Read more about the ATP’s methodology .

Technology use can be related to the way the survey is conducted. For example, our surveys in Malaysia, Singapore and South Korea are designed to only call mobile phone numbers and interview people on mobile phones because the prevalence of mobile phone ownership is so high. For instance, a 2021 study by the Korea Information Society Development Institute found that 97% of all people in Korea, not just adults, own a mobile phone.

In addition, people who take our survey over the phone may be more likely to use technology compared with those who take the survey in person. In 2019, we conducted simultaneous telephone and in-person surveys in Italy. Both samples were representative of the Italian population with respect to age, gender, education, and region. Respondents who took part in the telephone survey had somewhat higher rates of internet use, smartphone ownership and social media use. We moved from in-person interviews to telephone interviews in Italy in 2020 and Greece in 2021, and do not make direct comparisons to technology use prior to the mode change.

For purposes of comparison, data from Australia is not included in analyses of internet use or phone ownership. Internet use, smartphone and mobile phone ownership, and social media use data in the U.S. comes from a phone survey conducted Jan. 25 to Feb. 8, 2021.

Here are the questions used for the report, along with responses, and the survey methodology .

As people across the globe have increasingly turned to Facebook, Twitter, WhatsApp and other platforms to get their news and express their opinions, the sphere of social media has become a new public space for discussing – and often arguing bitterly – about political and social issues. And in the mind of many analysts, social media is one of the major reasons for the declining health of democracy in nations around the world.

However, as a new Pew Research Center survey of 19 advanced economies shows, ordinary citizens see social media as both a constructive and destructive component of political life, and overall most believe it has actually had a positive impact on democracy. Across the countries polled, a median of 57% say social media has been more of a good thing for their democracy, with 35% saying it has been a bad thing.

There are substantial cross-national differences on this question, however, and the United States is a clear outlier: Just 34% of U.S. adults think social media has been good for democracy, while 64% say it has had a bad impact. In fact, the U.S. is an outlier on a number of measures, with larger shares of Americans seeing social media as divisive.

Even in countries where assessments of social media’s impact are largely positive, most believe it has had some pernicious effects – in particular, it has led to manipulation and division within societies. A median of 84% across the 19 countries surveyed believe access to the internet and social media have made people easier to manipulate with false information and rumors. A recent analysis of the same survey shows that a median of 70% across the 19 nations consider the spread of false information online to be a major threat, second only to climate change on a list of global threats.

Additionally, a median of 65% think it has made people more divided in their political opinions. More than four-in-ten say it has made people less civil in how they talk about politics (only about a quarter say it has made people more civil).

So given the online world’s manipulation, divisiveness and lack of civility, what’s to like? How can this acrimonious sea of false information be good for democracy? Part of the answer may be that it gives people a sense of empowerment at a time when few feel empowered. Majorities in nearly every country surveyed say their political system does not allow people like them to have an influence in politics. In nine nations, including the U.S., seven-in-ten or more express that view.

Online platforms may help people feel less powerless in a few ways. First, social media informs them. As a recent Pew Research Center report highlighted, majorities in these countries believe that staying informed about domestic and international events is part of being a good citizen, and it is clear that people believe the internet and social media make it easier to stay informed. Nearly three-quarters say the internet and social media have made people more informed about current events in their own country as well as in other countries. Young adults are especially likely to hold these views. 

Also, most of those surveyed see social media as an effective tool for accomplishing political goals. Majorities in most countries say it is at least somewhat effective at raising public awareness, changing people’s minds about issues, getting elected officials to pay attention to issues and influencing policy decisions.

For some, social media is also an outlet for expression. In South Korea, for example, roughly half of social media users say they sometimes or often post or share things online about political or social issues. However, in the other countries polled, posting about these issues is less common, and in 12 nations four-in-ten or more say they never post about political or social topics. These are among the major findings of a Pew Research Center survey, conducted from Feb. 14 to June 3, 2022, among 24,525 adults in 19 nations.  

Americans most likely to say social media has been bad for democracy

Majorities in most of the nations surveyed believe social media has been a good thing for democracy in their country. Assessments are especially positive in Singapore, Malaysia, Poland, Sweden, Hungary and Israel, where 65% or more hold this view (for data on how international research organizations assess the quality of democracy in the countries surveyed, see Appendix A ).

In contrast, Americans are the most negative about the impact of social media on democracy: 64% say it has been bad. Republicans and independents who lean toward the Republican Party (74%) are much more likely than Democrats and Democratic leaners (57%) to see the ill effects of social media on the political system.

Half or more also say social media has been bad for democracy in the Netherlands, France and Australia.

In addition to being the most negative about social media’s influence on democracy, Americans are consistently among the most negative in their assessments of specific ways social media has affected politics and society. For example, 79% in the U.S. believe access to the internet and social media has made people more divided in their political opinions, the highest percentage among the 19 countries polled.

Similarly, 69% of Americans say the internet and social media have made people less civil in how they talk about politics – again the highest share among the nations in the study.

To compare how publics evaluate the impact of the internet and social media on society, we created an index that combines responses to six questions regarding whether the internet makes people: 1) less informed about current events in their country, 2) more divided in their political opinions, 3) less accepting of people from different backgrounds, 4) easier to manipulate with false information and rumors, 5) less informed about current events in other countries, and 6) less civil in the way they talk about politics.

The negative positions on all of these questions were coded as 1 while positive or “no impact” responses were coded as 0. For each respondent, scores on the overall index can range from 0, indicating they see no negative effects of the internet and social media across these questions, to 6, meaning a negative answer to all six questions. See Appendix B for more information about how the index was created.

Looking at the data this way illustrates the degree to which Americans stand out for their negative take on social media’s impact. The average score among U.S. respondents is 3.05, the highest – and therefore the most negative – in the survey. Dutch, Hungarian and Australian respondents are also more negative than others. In contrast, Malaysians, Israelis, Poles and Singaporeans offer less negative assessments. 

Pew Research Center’s research on the internet, social media and technology in the U.S. and around the world

Many of the topics explored in this report have been studied in depth in the U.S. by Pew Research Center’s internet and technology team , which for more than two decades has conducted survey research on the social impact of digital technologies, such as  internet and broadband ,  mobile connectivity  and  social media . The team’s work has included topics such as  privacy and surveillance ,  activism and civic engagement ,  digital divides ,  the role of technology in people’s lives and broader society ,  teens’ and younger children’s use of technology and  online dating . In addition, this research has examined the emergence of facial recognition, smart speakers, the gig/sharing economy, people’s attitudes about automation and algorithms and the use of wearable technology. The research has also regularly explored the future of digital life on such issues as the future of work and the rise of artificial intelligence.

The Center has also continually studied technology usage and views about the impact of digital technologies around the world as part of its Global Attitudes research, including reports on topics such as social media usage , smartphone ownership and public opinion in Africa regarding the impact of the internet on society. 

In 2018, the Center conducted an in-depth survey in 11 emerging economies, examining views about mobile technology and social media , as well as attitudes toward diversity in these nations. The Center also conducted focus groups in five countries as part of this study. In many ways, the results of the 2018 study were similar to those in the current survey, in that people in emerging and advanced economies alike believe social media presents both opportunities and dangers. For a comparison of results from the two studies, see “ In advanced and emerging economies, similar views on how social media affects democracy and society .”

For the past few years, the COVID-19 pandemic has created challenges for conducting surveys in nations where the Center typically interviews respondents in person, rather than via phone or online approaches. Moving forward, we will return to in-person interviewing in countries around the world, which will allow us to explore the impact of technology and other issues in regions that are underrepresented or not represented in this report.

The rapid growth of social media

Pew Research Center has been asking about social media usage for the past decade, and trend data from several nations polled over that time period highlights the extent to which these platforms have become pervasive in recent years. Growth has been especially dramatic in Japan, where just 30% used social media in 2012, compared with 75% today. Social media has also increased markedly in France, Poland, Spain, the U.S. and the United Kingdom. Even in Germany, which lags significantly behind these other nations in social media usage, there has been a notable increase since 2012. 

In every nation surveyed, young people are more likely than others to use social media. However, the age gap has closed over the past decade. When looking again at data from seven nations polled in both 2012 and 2022, growth in usage has been especially steep among 30- to 49-year-olds and those ages 50 and older. For example, nearly all British 18- to 29-year-olds were already social media users in 2012, but there has been significant growth among the two older age groups during the past 10 years.

Young people more likely to see benefits of social media

Overall, young adults are more likely than older adults to use the internet, own a smartphone and use social media. For more information on age differences in technology use, as well as differences by education and income, see the detailed tables accompanying this report.

In addition to using social media more than their older counterparts, young adults often stand out in their views about the impact of social media.

Adults ages 18 to 29 are more likely than those 50 and older to say social media has been good for democracy in 12 out of 19 nations surveyed. For instance, while 87% of 18- to 29-year-old Poles believe social media has had a positive effect on politics, just 46% of those 50 and older agree.

Young adults are also often more likely to say the internet and social media has made people more informed about domestic and international events, and they are especially likely to say these technologies have made people more accepting of others from different backgrounds.

In many cases, young people are also especially likely to consider social media an effective tool in the political realm, particularly regarding its capacity to change people’s minds on social issues and to raise awareness of those issues.

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72% of americans say the u.s. used to be a good example of democracy, but isn’t anymore, satisfaction with democracy has declined in recent years in high-income nations, more than 80% of americans believe elected officials don’t care what people like them think, support for democracy is strong in hong kong and taiwan, most popular, report materials.

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September 26, 2024

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A social media platform that is actually good for democracy?

by Phoebe Quinn, University of Melbourne

Technology is often seen as a threat to democracy, with the surge in AI capabilities the latest big concern.

This historic year of elections has put the complex relationship between technology and democracy on full display.

All eyes are currently on the upcoming elections in the U.S., with recent assassination attempts highlighting the deep divisions in the world's most powerful democracy.

Many have blamed social media for supercharging this polarization , with AI increasingly used to spread disinformation on these platforms.

But, instead of dividing, could "pro-democratic" social technologies help communities work through difficult issues like climate change?

I'm exploring how digital tools can support democracy by helping crowdsource ideas, understand lines of division, and find points of consensus that bridge them.

These trials are inspired by innovations in digital democracy in Taiwan, another democratic society under threat.

Lessons from Taiwan

Back in January, many held grave concerns about technology being weaponized to spread disinformation and influence the general election in Taiwan.

But these threats were fended off with overwhelming success , largely thanks to thoughtful and socially-oriented uses of digital technology.

Digital tools—created and used collaboratively by citizens, civil society and government—were also a pillar of Taiwan's response to the COVID-19 crisis, which was celebrated for achieving among the lowest mortality rates in the world without imposing lockdowns .

Examples include the use of social media to "inoculate" misinformation through funny memes , and an app created by civic hackers to show live mask availability in pharmacies.

This resilience to threats shows the power of the digital democracy ecosystem that has flourished in Taiwan over the past decade, according to a new book, " Plurality ," showcasing the island as a model for the future of collaborative technology and democracy.

Speaking at the National Press Club during a recent visit to Australia, one of the authors, Audrey Tang—civic hacker turned Taiwan's first digital minister—explained the ethos underpinning these efforts: that the best way to protect democracy is to keep improving it.

Polis—a 'pro-social' platform

Tang says we shouldn't be surprised when people start "screaming at each other" in political discussions on platforms like Facebook or X/Twitter because they are the digital equivalent of having a town hall discussion in a nightclub.

Polis is something different—an open-source digital democracy tool that Tang calls a "pro-social social media" platform .

To protect against trolling, there is no "reply" button—when you see a comment, you just vote (agree, disagree or pass) and can submit your own comment(s) separately if you want to.

In Taiwan, Polis has been used to find a "rough consensus" on issues like Uber regulation and AI development , inspiring many to wonder if these approaches could be used and built upon in other contexts around the world.

When I first heard these stories about five years ago, I was working with colleagues to support community well-being in the face of climate change and disasters. And we began to wonder:

• Could tools like Polis be useful in breaking the deadlock in Australia's seemingly never-ending "climate wars ?"
• Could they smooth the path for community members to be heard after disasters like bushfires , so that crucial issues and great ideas can make their way into the spotlight, and into action?

Given the many urgent, complex issues facing communities across Australia due to climate change, these are compelling possibilities. This has been the motivation for my doctoral research.

Lessons from climate change pilots in Australia

In our first pilot using Polis in Australia in 2022, we ran a consultation at the University of Melbourne about the climate impacts of staff flying—a thorny issue especially in countries like Australia that are far away from conference hubs in the U.S. and Europe.

We posed a single question: What should we do about staff air travel emissions?

Through the Polis consultation, 173 participants made more than 300 suggestions in their own words and voted over 22,000 times on each others' comments.

In this way, we crowdsourced a detailed picture of collective opinion. The Polis automated analysis tools displayed patterns of opinion to participants in real-time and then we did extra analysis for deeper understanding.

It was a contentious issue. About half the participants were eager for a wide range of measures to dramatically reduce staff flying, while for others the negative impacts of restricting flights were front of mind.

But through the Polis consultation, we found key points of consensus that were very popular across the board. One of these 'bridging' ideas was the principle that any policies on this issue should not exacerbate inequities.

Next, we partnered with a council in Melbourne's southeast to run a consultation on the issue of extreme heat .

This surfaced many ideas from community members—like reducing or banning artificial grass—that were broadly supported by participants. Views were mixed on many other comments, like a suggestion that new builds on private properties "should be required to keep all existing trees."

Interestingly, the issue of heat was also discussed in parallel on Facebook, where the council had shared a notice about the consultation.

Comments on the Facebook post often had a combative tone or rejected extreme heat as an issue worth discussing—much more so than in the Polis consultation itself.

There is plenty to unpack here, but this general lesson is clear: different online platforms can foster very different political discussions .

The Polis platform is far from perfect and our research explores the limitations and drawbacks in these case studies, as well as learning from what went well.

There is no silver bullet that can solve these huge societal challenges around democracy and climate change .

Instead, these initiatives add to a growing ecosystem of democratic innovations—from using new digital AI tools to in-person deliberative democracy through climate assemblies —which are being thoughtfully explored and combined around the world.

In this way, we can expand our collective capacity to navigate difficult issues and collaborate across diversity.

Provided by University of Melbourne

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