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Media Bias in News Report

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Published: Mar 19, 2024

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Introduction, origins of media bias, manifestations of media bias, implications of media bias, addressing media bias.

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Elizabeth Morrissette, Grace McKeon, Alison Louie, Amy Luther, and Alexis Fagen

Media bias could be defined as the unjust favoritism and reporting of a certain ideas or standpoint. In the news, social media, and entertainment, such as movies or television, we see media bias through the information these forms of media choose to pay attention to or report (“How to Detect Bias in News Media”, 2012). We could use the example of the difference between FOX news and CNN because these two news broadcasters have very different audiences, they tend to be biased to what the report and how they report it due to democratic or republican viewpoints.

Bias, in general, is the prejudice or preconceived notion against a person, group or thing. Bias leads to stereotyping which we can see on the way certain things are reported in the news. As an example, during Hurricane Katrina, there were two sets of photos taken of two people wading through water with bags of food. The people, one white and one black, were reported about but the way they were reported about was different. For the black man, he was reported “looting” a grocery store, while the white person was reported “finding food for survival”. The report showed media bias because they made the black man seem like he was doing something wrong, while the white person was just “finding things in order to survive” (Guarino, 2015).

Commercial media is affected by bias because a corporation can influence what kind of entertainment is being produced. When there is an investment involved or money at stake, companies tend to want to protect their investment by not touching on topics that could start a controversy (Pavlik, 2018). In order to be able to understand what biased news is, we must be media literate. To be media literate, we need to adopt the idea that news isn’t completely transparent in the stories they choose to report. Having the knowledge that we can’t believe everything we read or see on the news will allow us as a society to become a more educated audience (Campbell, 2005).

Bias in the News

The news, whether we like it or not, is bias. Some news is bias towards Republicans while other news outlets are biased towards Democrats. It’s important to understand this when watching or reading the news to be media literate. This can be tricky because journalists may believe that their reporting is written with “fairness and balance” but most times there is an underlying bias based around what news provider the story is being written for (Pavlik and McIntosh, 61). With events happening so rapidly, journalist write quickly and sometimes point fingers without trying to. This is called Agenda-Setting which is defined by Shirley Biagi as, how reporters neglect to tell people what to think, but do tell them what and who to talk about (Biagi, 268).

The pressure to put out articles quickly, often times, can affect the story as well. How an event is portrayed, without all the facts and viewpoints, can allow the scene to be laid out in a way that frames it differently than it may have happened (Biagi, 269). However, by simply watching or reading only one portrayal of an event people will often blindly believe it is true, without see or reading other stories that may shine a different light on the subject (Vivian, 4). Media Impact defines this as Magic Bullet Theory or the assertion that media messages directly and measurably affect people’s behavior (Biagi, 269). The stress of tight time deadlines also affects the number of variations of a story. Journalist push to get stories out creates a lack of deeper consideration to news stories. This is called Consensus Journalism or the tendency among journalists covering the same topic to report similar articles instead of differing interpretations of the event (Biagi, 268).

To see past media bias in the news it’s important to be media literate. Looking past any possible framing, or bias viewpoints and getting all the facts to create your own interpretation of a news story. It doesn’t hurt to read both sides of the story before blindly following what someone is saying, taking into consideration who they might be biased towards.

Stereotypes in the Media

Bias is not only in the news, but other entertainment media outlets such as TV and movies. Beginning during childhood, our perception of the world starts to form. Our own opinions and views are created as we learn to think for ourselves. The process of this “thinking for ourselves” is called socialization. One key agent of socialization is the mass media. Mass media portrays ideas and images that at such a young age, are very influential. However, the influence that the media has on us is not always positive. Specifically, the entertainment media, plays a big role in spreading stereotypes so much that they become normal to us (Pavlik and McIntosh, 55).

The stereotypes in entertainment media may be either gender stereotypes or cultural stereotypes. Gender stereotypes reinforce the way people see each gender supposed to be like. For example, a female stereotype could be a teenage girl who likes to go shopping, or a stay at home mom who cleans the house and goes grocery shopping. Men and women are shown in different ways in commercials, TV and movies. Women are shown as domestic housewives, and men are shown as having high status jobs, and participating in more outdoor activities (Davis, 411). A very common gender stereotype for women is that they like to shop, and are not smart enough to have a high-status profession such as a lawyer or doctor. An example of this stereotype can be shown in the musical/movie, Legally Blonde. The main character is woman who is doubted by her male counterparts. She must prove herself to be intelligent enough to become a lawyer. Another example of a gender stereotype is that men like to use tools and drive cars. For example, in most tool and car commercials /advertisements, a man is shown using the product. On the other hand, women are most always seen in commercials for cleaning supplies or products like soaps. This stems the common stereotype that women are stay at home moms and take on duties such as cleaning the house, doing the dishes, doing the laundry, etc.

Racial stereotyping is also quite common in the entertainment media. The mass media helps to reproduce racial stereotypes, and spread those ideologies (Abraham, 184). For example, in movies and TV, the minority characters are shown as their respective stereotypes. In one specific example, the media “manifests bias and prejudice in representations of African Americans” (Abraham, 184). African Americans in the media are portrayed in negative ways. In the news, African Americans are often portrayed to be linked to negative issues such as crime, drug use, and poverty (Abraham 184). Another example of racial stereotyping is Kevin Gnapoor in the popular movie, Mean Girls . His character is Indian, and happens to be a math enthusiast and member of the Mathletes. This example strongly proves how entertainment media uses stereotypes.

Types of Media Bias

Throughout media, we see many different types of bias being used. These is bias by omission, bias by selection of source, bias by story selection, bias by placement, and bias by labeling. All of these different types are used in different ways to prevent the consumer from getting all of the information.

Bias by omission: Bias by omission is when the reporter leaves out one side of the argument, restricting the information that the consumer receives. This is most prevalent when dealing with political stories (Dugger) and happens by either leaving out claims from either the liberal or conservative sides. This can be seen in either one story or a continuation of stories over time (Media Bias). There are ways to avoid this type of bias, these would include reading or viewing different sources to ensure that you are getting all of the information.
Bias by selection of sources: Bias by selection of sources occurs when the author includes multiple sources that only have to do with one side (Baker). Also, this can occur when the author intentionally leaves out sources that are pertinent to the other side of the story (Dugger). This type of bias also utilizes language such as “experts believe” and “observers say” to make people believe that what they are reading is credible. Also, the use of expert opinions is seen but only from one side, creating a barrier between one side of the story and the consumers (Baker).
Bias by story selection: The second type of bias by selection is bias by story selection. This is seen more throughout an entire corporation, rather than through few stories. This occurs when news broadcasters only choose to include stories that support the overall belief of the corporation in their broadcasts. This means ignoring all stories that would sway people to the other side (Baker). Normally the stories that are selected will fully support either the left-wing or right-wing way of thinking.
Bias by placement: Bias by placement is a big problem in today’s society. We are seeing this type of bias more and more because it is easy with all of the different ways media is presented now, either through social media or just online. This type of bias shows how important a particular story is to the reporter. Editors will choose to poorly place stories that they don’t think are as important, or that they don’t want to be as easily accessible. This placement is used to downplay their importance and make consumers think they aren’t as important (Baker).
Bias by labeling: Bias by labeling is a more complicated type of bias mostly used to falsely describe politicians. Many reporters will tag politicians with extreme labels on one side of an argument while saying nothing about the other side (Media Bias). These labels that are given can either be a good thing or a bad thing, depending on the side they are biased towards. Some reporters will falsely label people as “experts”, giving them authority that they have not earned and in turn do not deserve (Media Bias). This type of bias can also come when a reporter fails to properly label a politician, such as not labeling a conservative as a conservative (Dugger). This can be difficult to pick out because not all labeling is biased, but when stronger labels are used it is important to check different sources to see if the information is correct.

Bias in Entertainment

Bias is an opinion in favor or against a person, group, and or thing compared to another, and are presented, in such ways to favor false results that are in line with their prejudgments and political or practical commitments (Hammersley & Gomm, 1). Media bias in the entertainment is the bias from journalists and the news within the mass media about stories and events reported and the coverage of them.

There are biases in most entertainment today, such as, the news, movies, and television. The three most common biases formed in entertainment are political, racial, and gender biases. Political bias is when part of the entertainment throws in a political comment into a movie or TV show in hopes to change or detriment the viewers political views (Murillo, 462). Racial bias is, for example, is when African Americans are portrayed in a negative way and are shown in situations that have to do with things such as crime, drug use, and poverty (Mitchell, 621). Gender biases typically have to do with females. Gender biases have to do with roles that some people play and how others view them (Martin, 665). For example, young girls are supposed to be into the color pink and like princess and dolls. Women are usually the ones seen on cleaning commercials. Women are seen as “dainty” and “fragile.” And for men, they are usually seen on the more “masculine types of media, such as things that have to do with cars, and tools.

Bias is always present, and it can be found in all outlets of media. There are so many different types of bias that are present, whether it is found in is found in the news, entertainment industry, or in the portrayal of stereotypes bias, is all around us. To be media literate it’s important to always be aware of this, and to read more than one article, allowing yourself to come up with conclusion; thinking for yourself.

Works Cited

Abraham, Linus, and Osei Appiah. “Framing News Stories: The Role of Visual Imagery in Priming Racial Stereotypes.” Howard Journal of Communications , vol. 17, no. 3, 2006, pp. 183–203.

Baker, Brent H. “Media Bias.” Student News Daily , 2017.

Biagi, Shirley. “Changing Messages.” Media/Impact; An Introduction to Mass Media , 10th ed., Cengage Learning, 2013, pp. 268-270.

Campbell, Richard, et al. Media & Culture: an Introduction to Mass Communication . Bedford/St Martins, 2005.

Davis, Shannon N. “Sex Stereotypes In Commercials Targeted Toward Children: A Content Analysis.” Sociological Spectrum , vol. 23, no. 4, 2003, pp. 407–424.

Dugger, Ashley. “Media Bias and Criticism .” http://study.com/academy/lesson/media-bias-criticism-definition-types-examples.html .

Guarino, Mark. “Misleading reports of lawlessness after Katrina worsened crisis, officials say.” The Guardian , 16 Aug. 2015, http://www.theguardian.com/us-news/2015/aug/16/hurricane-katrina-new-orleans-looting-violence-misleading-reports .

Hammersley, Martyn, and Roger Gomm. Bias in Social Research . Vol. 2, ser. 1, Sociological Research Online, 1997.

“How to Detect Bias in News Media.” FAIR , 19 Nov. 2012, http://fair.org/take-action-now/media-activism-kit/how-to-detect-bias-in-news-media/ .

Levasseur, David G. “Media Bias.” Encyclopedia of Political Communication , Lynda Lee Kaid, editor, Sage Publications, 1st edition, 2008. Credo Reference, https://search.credoreference.com/content/entry/sagepolcom/media_bias/0 .

Martin, Patricia Yancey, John R. Reynolds, and Shelley Keith, “Gender Bias and Feminist Consciousness among Judges and Attorneys: A Standpoint Theory Analysis,” Signs: Journal of Women in Culture and Society 27, no. 3 (Spring 2002): 665-701,

Mitchell, T. L., Haw, R. M., Pfeifer, J. E., & Meissner, C. A. (2005). “Racial Bias in Mock Juror Decision-Making: A Meta-Analytic Review of Defendant Treatment.” Law and Human Behavior , 29(6), 621-637.

Murillo, M. (2002). “Political Bias in Policy Convergence: Privatization Choices in Latin America.” World Politics , 54(4), 462-493.

Pavlik, John V., and Shawn McIntosh. “Media Literacy in the Digital Age .” Converging Media: a New Introduction to Mass Communication , Oxford University Press, 2017.

Vivian, John. “Media Literacy .” The Media of Mass Communication , 8th ed., Pearson, 2017, pp. 4–5.

Introduction to Media Studies Copyright © by Elizabeth Morrissette, Grace McKeon, Alison Louie, Amy Luther, and Alexis Fagen is licensed under a Creative Commons Attribution 4.0 International License , except where otherwise noted.

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Should you trust media bias charts?

These controversial charts claim to show the political lean and credibility of news organizations. here’s what you need to know about them..

Impartial journalism is an impossible ideal. That is, at least, according to Julie Mastrine.

“Unbiased news doesn’t exist. Everyone has a bias: everyday people and journalists. And that’s OK,” Mastrine said. But it’s not OK for news organizations to hide those biases, she said.

“We can be manipulated into (a biased outlet’s) point of view and not able to evaluate it critically and objectively and understand where it’s coming from,” said Mastrine, marketing director for AllSides , a media literacy company focused on “freeing people from filter bubbles.”

That’s why she created a media bias chart.

As readers hurl claims of hidden bias towards outlets on all parts of the political spectrum, bias charts have emerged as a tool to reveal pernicious partiality.

Charts that use transparent methodologies to score political bias — particularly the AllSides chart and another from news literacy company Ad Fontes Media — are increasing in popularity and spreading across the internet. According to CrowdTangle, a social media monitoring platform, the homepages for these two sites and the pages for their charts have been shared tens of thousands of times.

But just because something is widely shared doesn’t mean it’s accurate. Are media bias charts reliable?

Why do media bias charts exist?

Traditional journalism values a focus on news reporting that is fair and impartial, guided by principles like truth, verification and accuracy. But those standards are not observed across the board in the “news” content that people consume.

Tim Groeling, a communications professor at the University of California Los Angeles, said some consumers take too much of the “news” they encounter as impartial.

When people are influenced by undisclosed political bias in the news they consume, “that’s pretty bad for democratic politics, pretty bad for our country to have people be consistently misinformed and think they’re informed,” Groeling said.

If undisclosed bias threatens to mislead some news consumers, it also pushes others away, he said.

“When you have bias that’s not acknowledged, but is present, that’s really damaging to trust,” he said.

Kelly McBride, an expert on journalism ethics and standards, NPR’s public editor and the chair of the Craig Newmark Center for Ethics and Leadership at Poynter, agrees.

“If a news consumer doesn’t see their particular bias in a story accounted for — not necessarily validated, but at least accounted for in a story — they are going to assume that the reporter or the publication is biased,” McBride said.

The growing public confusion about whether or not news outlets harbor a political bias, disclosed or not, is fueling demand for resources to sort fact from otherwise — resources like these media bias charts.

Bias and social media

Mastrine said the threat of undisclosed biases grows as social media algorithms create filter bubbles to feed users ideologically consistent content.

Could rating bias help? Mastrine and Vanessa Otero, founder of the Ad Fontes media bias chart, think so.

“It’ll actually make it easier for people to identify different perspectives and make sure they’re reading across the spectrum so that they get a balanced understanding of current events,” Mastrine said.

Otero said bias ratings could also be helpful to advertisers.

“There’s this whole ecosystem of online junk news, of polarizing misinformation, these clickbaity sites that are sucking up a lot of ad revenue. And that’s not to the benefit of anybody,” Otero said. “It’s not to the benefit of the advertisers. It’s not to the benefit of society. It’s just to the benefit of some folks who want to take advantage of people’s worst inclinations online.”

Reliable media bias ratings could allow advertisers to disinvest in fringe sites.

Groeling, the UCLA professor, said he could see major social media and search platforms using bias ratings to alter the algorithms that determine what content users see. Changes could elevate neutral content or foster broader news consumption.

But he fears the platforms’ sweeping power, especially after Facebook and Twitter censored a New York Post article purporting to show data from a laptop belonging to Hunter Biden, the son of President-elect Joe Biden. Groeling said social media platforms failed to clearly communicate how and why they stopped and slowed the spread of the article.

“(Social media platforms are) searching for some sort of arbiter of truth and news … but it’s actually really difficult to do that and not be a frightening totalitarian,” he said.

Is less more?

The Ad Fontes chart and the AllSides chart are each easy to understand: progressive publishers on one side, conservative ones on the other.

“It’s just more visible, more shareable. We think more people can see the ratings this way and kind of begin to understand them and really start to think, ‘Oh, you know, journalism is supposed to be objective and balanced,’” Mastrine said. AllSides has rated media bias since 2012. Mastrine first put them into chart form in early 2019.

Otero recognizes that accessibility comes at a price.

“Some nuance has to go away when it’s a graphic,” she said. “If you always keep it to, ‘people can only understand if they have a very deep conversation,’ then some people are just never going to get there. So it is a tool to help people have a shortcut.”

But perceiving the chart as distilled truth could give consumers an undue trust in outlets, McBride said.

“Overreliance on a chart like this is going to probably give some consumers a false level of faith,” she said. “I can think of a massive journalistic failure for just about every organization on this chart. And they didn’t all come clean about it.”

The necessity of getting people to look at the chart poses another challenge. Groeling thinks disinterest among consumers could hurt the charts’ usefulness.

“Asking people to go to this chart, asking them to take effort to understand and do that comparison, I worry would not actually be something people would do. Because most people don’t care enough about news,” he said. He would rather see a plugin that detects bias in users’ overall news consumption and offers them differing viewpoints.

McBride questioned whether bias should be the focus of the charts at all. Other factors — accountability, reliability and resources — would offer better insight into what sources of news are best, she said.

“Bias is only one thing that you need to pay attention to when you consume news. What you also want to pay attention to is the quality of the actual reporting and writing and the editing,” she said. It wouldn’t make sense to rate local news sources for bias, she added, because they are responsive to individual communities with different political ideologies.

The charts are only as good as their methodologies. Both McBride and Groeling shared praise for the stated methods for rating bias of AllSides and Ad Fontes , which can be found on their websites. Neither Ad Fontes nor AllSides explicitly rates editorial standards.

The AllSides Chart

(Courtesy: AllSides)

The AllSides chart focuses solely on political bias. It places sources in one of five boxes — “Left,” “Lean Left,” “Center,” “Lean Right” and “Right.” Mastrine said that while the boxes allow the chart to be easily understood, they also don’t allow sources to be rated on a gradient.

“Our five-point scale is inherently limited in the sense that we have to put somebody in a category when, in reality, it’s kind of a spectrum. They might fall in between two of the ratings,” Mastrine said.

That also makes the chart particularly easy to understand, she said.

AllSides has rated more than 800 sources in eight years, focusing on online content only. Ratings are derived from a mix of review methods.

In the blind bias survey, which Mastrine called “one of (AllSides’) most robust bias rating methodologies,” readers from the public rate articles for political bias. Two AllSides staffers with different political biases pull articles from the news sites that are being reviewed. AllSides locates these unpaid readers through its newsletter, website, social media account and other marketing tools. The readers, who self-report their political bias after they use a bias rating test provided by the company, only see the article’s text and are not told which outlet published the piece. The data is then normalized to more closely reflect the composure of America across political groupings.

AllSides also uses “editorial reviews,” where staff members look directly at a source to contribute to ratings.

“That allows us to actually look at the homepage with the branding, with the photos and all that and kind of get a feel for what the bias is, taking all that into account,” Mastrine said.

She added that an equal number of staffers who lean left, right and center conduct each review together. The personal biases of AllSides’ staffers appear on their bio pages . Mastrine leans right.

She clarified that among the 20-person staff, many are part time, 14% are people of color, 38% are lean left or left, 29% are center, and 18% are lean right or right. Half of the staffers are male, half are female.

When a news outlet receives a blind bias survey and an editorial review, both are taken into account. Mastrine said the two methods aren’t weighted together “in any mathematical way,” but said they typically hold roughly equal weight. Sometimes, she added, the editorial review carries more weight.

AllSides also uses “independent research,” which Mastrine described as the “lowest level of bias verification.” She said it consists of staffers reviewing and reporting on a source to make a preliminary bias assessment. Sometimes third-party analyses — including academic research and surveys — are incorporated into ratings, too.

AllSides highlights the specific methodologies used to judge each source on its website and states its confidence in the ratings based on the methods used. In a separate white paper , the company details the process used for its August 2020 blind bias survey.

AllSides sometimes gives separate ratings to different sections of the same source. For example, it rates The New York Times’ opinion section “Left” and its news section “Lean Left.” AllSides also incorporates reader feedback into its system. People can mark that they agree or disagree with AllSides’ rating of a source. When a significant number of people disagree, AllSides often revisits a source to vet it once again, Mastrine said.

The AllSides chart generally gets good reviews, she said, and most people mark that they agree with the ratings. Still, she sees one misconception among the people that encounter it: They think center means better. Mastrine disagrees.

“The center outlets might be omitting certain stories that are important to people. They might not even be accurate,” she said. “We tell people to read across the spectrum.”

To make that easier, AllSides offers a curated “ balanced news feed ,” featuring articles from across the political spectrum, on its website.

AllSides makes money through paid memberships, one-time donations, media literacy training and online advertisements. It plans to become a public benefit corporation by the end of the year, she added, meaning it will operate both for profit and for a stated public mission.

The Ad Fontes chart

(Courtesy: Ad Fontes)

The Ad Fontes chart rates both reliability and political bias. It scores news sources — around 270 now, and an expected 300 in December — using bias and reliability as coordinates on its chart.

The outlets appear on a spectrum, with seven markers showing a range from “Most Extreme Left” to “Most Extreme Right” along the bias axis, and eight markers showing a range from “Original Fact Reporting” to “Contains Inaccurate/Fabricated Info” along the reliability axis.

The chart is a departure from its first version, back when founder Vanessa Otero , a patent attorney, said she put together a chart by herself as a hobby after seeing Facebook friends fight over the legitimacy of sources during the 2016 election. Otero said that when she saw how popular her chart was, she decided to make bias ratings her full-time job and founded Ad Fontes — Latin for “to the source” — in 2018.

“There were so many thousands of people reaching out to me on the internet about this,” she said. “Teachers were using it in their classrooms as a tool for teaching media literacy. Publishers wanted to publish it in textbooks.”

About 30 paid analysts rate articles for Ad Fontes. Listed on the company’s website , they represent a range of experience — current and former journalists, educators, librarians and similar professionals. The company recruits analysts through its email list and references and vets them through a traditional application process. Hired analysts are then trained by Otero and other Ad Fontes staff.

To start review sessions, a group of coordinators composed of senior analysts and the company’s nine staffers pulls articles from the sites being reviewed. They look for articles listed as most popular or displayed most prominently.

Part of the Ad Fontes analyst political bias test. The test asks analysts to rank their political bias on 18 different policy issues.

Ad Fontes administers an internal political bias test to analysts, asking them to rank their left-to-right position on about 20 policy positions. That information allows the company to attempt to create ideological balance by including one centrist, one left-leaning and one right-leaning analyst on each review panel. The panels review at least three articles for each source, but they may review as many as 30 for particularly prominent outlets, like The Washington Post, Otero said. More on their methodology, including how they choose which articles to review to create a bias rating, can be found here on the Ad Fontes website.

When they review the articles, the analysts see them as they appear online, “because that’s how people encounter all content. No one encounters content blind,” Otero said. The review process recently changed so that paired analysts discuss their ratings over video chat, where they are pushed to be more specific as they form ratings, Otero said.

Individual scores for an article’s accuracy, the use of fact or opinion, and the appropriateness of its headline and image combine to create a reliability score. The bias score is determined by the article’s degree of advocacy for a left-to-right political position, topic selection and omission, and use of language.

To create an overall bias and reliability score for an outlet, the individual scores for each reviewed article are averaged, with added importance given to more popular articles. That average determines where sources show up on the chart.

Ad Fontes details its ratings process in a white paper from August 2019.

While the company mostly reviews prominent legacy news sources and other popular news sites, Otero hopes to add more podcasts and video content to the chart in coming iterations. The chart already rates video news channel “ The Young Turks ” (which claims to be the most popular online news show with 250 million views per month and 5 million subscribers on YouTube ), and Otero mentioned she next wants to examine videos from Prager University (which claims 4 billion lifetime views for its content, has 2.84 million subscribers on YouTube and 1.4 million followers on Instagram ). Ad Fontes is working with ad agency Oxford Road and dental care company Quip to create ratings for the top 50 news and politics podcasts on Apple Podcasts, Otero said.

“It’s not strictly traditional news sources, because so much of the information that people use to make decisions in their lives is not exactly news,” Otero said.

She was shocked when academic textbook publishers first wanted to use her chart. Now she wants it to become a household tool.

“As we add more news sources on to it, as we add more data, I envision this becoming a standard framework for evaluating news on at least these two dimensions of reliability and bias,” she said.

She sees complaints about it from both ends of the political spectrum as proof that it works.

“A lot of people love it and a lot of people hate it,” Otero said. “A lot of people on the left will call us neoliberal shills, and then a bunch of people that are on the right are like, ‘Oh, you guys are a bunch of leftists yourselves.’”

The project has grown to include tools for teaching media literacy to school kids and an interactive version of the chart that displays each rated article. Otero’s company operates as a public benefit corporation with a stated public benefit mission: “to make news consumers smarter and news media better.” She didn’t want Ad Fontes to rely on donations.

“If we want to grow with a problem, we have to be a sustainable business. Otherwise, we’re just going to make a small difference in a corner of the problem,” she said.

Ad Fontes makes money by responding to specific research requests from advertisers, academics and other parties that want certain outlets to be reviewed. The company also receives non-deductible donations and operates on WeFunder , a grassroots crowdfunding investment site, to bring in investors. So far, Ad Fontes has raised $163,940 with 276 investors through the site.

Should you use the charts?

Media bias charts with transparent, rigorous methodologies can offer insight into sources’ biases. That insight can help you understand what perspectives sources bring as they share the news. That insight also might help you understand what perspectives you might be missing as a news consumer.

But use them with caution. Political bias isn’t the only thing news consumers should look out for. Reliability is critical, too, and the accuracy and editorial standards of organizations play an important role in sharing informative, useful news.

Media bias charts are a media literacy tool. They offer well-researched appraisals on the bias of certain sources. But to best inform yourself, you need a full toolbox. Check out Poynter’s MediaWise project for more media literacy tools.

This article was originally published on Dec. 14, 2020.

More about media bias charts

A media bias chart update puts The New York Times in a peculiar position
Letter to the editor: What Poynter’s critique misses about the Media Bias Chart

What I learned from writing a book about mental health for journalists

It publishes at a time when the levels of stress and pressure faced by many of our colleagues need to be taken far more seriously than they have been

Why it’s unlikely that Trump will lose his voting rights if convicted of a felony in New York

Former President Donald Trump is expected to soon face a jury verdict on felony falsifying business record charges

Advice for old(er) journalists

Early-career folks have some thoughts.

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Comments are closed.

We are too obsessed with alleged bias and objectivity, which so often is in the biased eye of the beholder. The main standard of good journalism should be verifiable factual accuracy.

Hoping to see a follow-up article about whether we can trust fact checker report card charts created by collecting a fact checker’s subjective ratings.

As a writer for Wonkette, I won’t claim to be objective, but we do like to point out that our rating at Ad Fontes – both farthest to the left and the least reliable, is absurd. Apparently we can’t be trusted at all because we do satirical commentary instead of straight news.

When we’ve attempted to point out to Ms. Otero that we adhere to high standards when it comes to factuality, but we also make jokes, she has replied that satire is inherently untrustworthy and biased, particularly since we sometimes use dirty words.

That seems to us a remarkably biased definition of bias.

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Open access
Published: 22 May 2024

Uncovering the essence of diverse media biases from the semantic embedding space

Hong Huang 1 , 2 , 3 , 4 , 5 ,
Hua Zhu 1 , 2 , 3 , 4 , 5 ,
Wenshi Liu 4 , 5 ,
Hua Gao 5 ,
Hai Jin 1 , 2 , 3 , 4 , 5 &
Bang Liu 6

Humanities and Social Sciences Communications volume 11 , Article number: 656 ( 2024 ) Cite this article

Metrics details

Cultural and media studies

Media bias widely exists in the articles published by news media, influencing their readers’ perceptions, and bringing prejudice or injustice to society. However, current analysis methods usually rely on human efforts or only focus on a specific type of bias, which cannot capture the varying magnitudes, connections, and dynamics of multiple biases, thus remaining insufficient to provide a deep insight into media bias. Inspired by the Cognitive Miser and Semantic Differential theories in psychology, and leveraging embedding techniques in the field of natural language processing, this study proposes a general media bias analysis framework that can uncover biased information in the semantic embedding space on a large scale and objectively quantify it on diverse topics. More than 8 million event records and 1.2 million news articles are collected to conduct this study. The findings indicate that media bias is highly regional and sensitive to popular events at the time, such as the Russia-Ukraine conflict. Furthermore, the results reveal some notable phenomena of media bias among multiple U.S. news outlets. While they exhibit diverse biases on different topics, some stereotypes are common, such as gender bias. This framework will be instrumental in helping people have a clearer insight into media bias and then fight against it to create a more fair and objective news environment.

Testing theory of mind in large language models and humans

Determinants of behaviour and their efficacy as targets of behavioural change interventions

Toolbox of individual-level interventions against online misinformation

Introduction.

In the era of information explosion, news media play a crucial role in delivering information to people and shaping their minds. Unfortunately, media bias, also called slanted news coverage, can heavily influence readers’ perceptions of news and result in a skewing of public opinion (Gentzkow et al. 2015 ; Puglisi and Snyder Jr, 2015b ; Sunstein, 2002 ). This influence can potentially lead to severe societal problems. For example, a report from FAIR has shown that Verizon management is more than twice as vocal as worker representatives in news reports about the Verizon workers’ strike in 2016 Footnote 1 , putting workers at a disadvantage in the news and contradicting the principles of fair and objective journalism. Unfortunately, this is just the tip of the media bias iceberg.

Media bias can be defined as the bias of journalists and news producers within the mass media in selecting and covering numerous events and stories (Gentzkow et al. 2015 ). This bias can manifest in various forms, such as event selection, tone, framing, and word choice (Hamborg et al. 2019 ; Puglisi and Snyder Jr, 2015b ). Given the vast number of events happening in the world at any given moment, even the most powerful media must be selective in what they choose to report instead of covering all available facts in detail (Downs, 1957 ). This selectivity can result in the perception of bias in the news coverage, whether intentional or unintentional. Academics in journalism studies attempt to explain the news selection process by developing taxonomies of news values (Galtung and Ruge, 1965 ; Harcup and O’neill, 2001 , 2017 ), which refer to certain criteria and principles that news editors and journalists consider when selecting, editing, and reporting the news. These values help determine which stories should be considered news and the significance of these stories in news reporting. However, different news organizations and journalists may emphasize different news values based on their specific objectives and audience. Consequently, a media outlet may be very keen on reporting events about specific topics while turning a blind eye to others. For example, news coverage often ignores women-related events and issues with the implicit assumption that they are less critical than men-related contents (Haraldsson and Wängnerud, 2019 ; Lühiste and Banducci, 2016 ; Ross and Carter, 2011 ). Once events are selected, the media must consider how to organize and write their news articles. At that time, the choice of tone, framing, and word is highly subjective and can introduce bias. Specifically, the words used by the authors to refer to different entities may not be neutral but instead imply various associations and value judgments (Puglisi and Snyder Jr, 2015b ). As shown in Fig. 1 , the same topic can be expressed in entirely different ways, depending on a media outlet’s standpoint Footnote 2 . For example, certain “right-wing” media outlets tend to support legal abortion, while some “left-wing” ones oppose it.

The blue and red fonts represent the views of some “left-wing” and “right-wing” media outlets, respectively.

In fact, media bias is influenced by many factors: explicit factors such as geographic location, media position, editorial guideline, topic setting, and so on; obscure factors such as political ideology (Groseclose and Milyo, 2005 ; MacGregor, 1997 ; Merloe, 2015 ), business reason (Groseclose and Milyo, 2005 ; Paul and Elder, 2004 ), and personal career (Baron, 2006 ), etc. Besides, some studies also summarize these factors related to bias as supply-side and demand-side ones (Gentzkow et al. 2015 ; Puglisi and Snyder Jr, 2015b ). The influence of these complex factors makes the emergence of media bias inevitable. However, media bias may hinder readers from forming objective judgments about the real world, lead to skewed public opinion, and even exacerbate social prejudices and unfairness. For example, the New York Times supports Iranian women’s saying no to hijabs in defense of women’s rights Footnote 3 while criticizing the Chinese government’s initiative to encourage Uyghur women to remove hijabs and veils Footnote 4 . Besides, the influence of news coverage on voter behavior is a subject of ongoing debate. While some studies indicate that slanted news coverage can influence voters and election outcomes (Bovet and Makse, 2019 ; DellaVigna and Kaplan, 2008 ; Grossmann and Hopkins, 2016 ), others suggest that this influence is limited in certain circumstances (Stroud, 2010 ). Fortunately, research on media bias has drawn attention from multiple disciplines.

In social science, the study of media bias has a long tradition dating back to the 1950s (White, 1950 ). So far, most of the analyses in social science have been qualitative, aiming to analyze media opinions expressed in the editorial section (e.g., endorsements (Ansolabehere et al. 2006 ), editorials (Ho et al. 2008 ), ballot propositions (Puglisi and Snyder Jr, 2015a )) or find out biased instances in news articles by human annotations (Niven, 2002 ; Papacharissi and de Fatima Oliveira, 2008 ; Vaismoradi et al. 2013 ). Some researchers also conduct quantitative analysis, which primarily involves counting the frequency of specific keywords or articles related to certain issues (D’Alessio and Allen, 2000 ; Harwood and Garry, 2003 ; Larcinese et al. 2011 ). In particular, there are some attempts to estimate media bias using automatic tools (Groseclose and Milyo, 2005 ), and they commonly rely on text similarity and sentiment computation (Gentzkow and Shapiro, 2010 ; Gentzkow et al. 2006 ; Lott Jr and Hassett, 2014 ). In summary, social science research on media bias has yielded extensive and effective methodologies. These methodologies interpret media bias from diverse perspectives, marking significant progress in the realm of media studies. However, these methods usually rely on manual annotation and analysis of the texts, which requires significant manual effort and expertise (Park et al. 2009 ), thus might be inefficient and subjective. For example, in a quantitative analysis, researchers might devise a codebook with detailed definitions and rules for annotating texts, and then ask coders to read and annotate the corresponding texts (Hamborg et al. 2019 ). Developing a codebook demands substantial expertise. Moreover, the standardization process for text annotation is subjective, as different coders may interpret the same text differently, thus leading to varied annotations.

In computer science, research on social media is extensive (Lazaridou et al. 2020 ; Liu et al. 2021b ; Tahmasbi et al. 2021 ), but few methods are specifically designed to study media bias (Hamborg et al. 2019 ). Some techniques that specialize in the study of media bias focus exclusively on one type of bias (Huang et al. 2021 ; Liu et al. 2021b ; Zhang et al. 2017 ), thus not general enough. In natural language processing (NLP), research on the bias of pre-trained models or language models has attracted much attention (Qiang et al. 2023 ), aiming to identify and reduce the potential impact of bias in pre-trained models on downstream tasks (Huang et al. 2020 ; Liu et al. 2021a ; Wang et al. 2020 ). In particular, some studies on pre-trained word embedding models show that they have captured rich human knowledge and biases (Caliskan et al. 2017 ; Grand et al. 2022 ; Zeng et al. 2023 ). However, such works mainly focus on pre-trained models rather than media bias directly, which limits their applicability to media bias analysis.

A major challenge in studying media bias is that the evaluation of media bias is highly subjective because individuals have varying evaluation criteria for bias. Take political bias as an example, a story that one person views as neutral may appear to be left-leaning or right-leaning by someone else. To address this challenge, we develop an objective and comprehensive media bias analysis framework. We study media bias from two distinct but highly relevant perspectives: the macro level and the micro level. From the macro perspective, we focus on the event selection bias of each media, i.e., the types of events each media tends to report on. From the micro perspective, we focus on the bias introduced by media in the choice of words and sentence construction when composing news articles about the selected events.

In news articles, media outlets convey their attitudes towards a subject through the contexts surrounding it. However, the language used by the media to describe and refer to entities may not be purely neutral descriptors but rather imply various associations and value judgments. According to the cognitive miser theory in psychology, the human mind is considered a cognitive miser who tends to think and solve problems in simpler and less effortful ways to avoid cognitive effort (Fiske and Taylor, 1991 ; Stanovich, 2009 ). Therefore, faced with endless news information, ordinary readers will tend to summarize and remember the news content simply, i.e., labeling the things involved in news reports. Frequent association of certain words with a particular entity or subject in news reports can influence a media outlet’s loyal readers to adopt these words as labels for the corresponding item in their cognition due to the cognitive miser effect. Unfortunately, such a cognitive approach is inadequate and susceptible to various biases. For instance, if a media outlet predominantly focuses on male scientists while neglecting their female counterparts, some naive readers may perceive scientists to be mostly male, leading to a recognition bias in their perception of the scientist and even forming stereotypes unconsciously over time. According to the “distributional hypothesis” in modern linguistics (Firth, 1957 ; Harris, 1954 ; Sahlgren, 2008 ), a word’s meaning is characterized by the words occurring in the same context as it. Here, we simplify the complex associations between different words (or entities/subjects) and their respective context words into co-occurrence relationships. An effective technique to capture word semantics based on co-occurrence information is neural network-based word embedding models (Kenton and Toutanova, 2019 ; Le and Mikolov, 2014 ; Mikolov et al. 2013 ).

Word embedding models represent each word in the vocabulary as a vector (i.e., word embedding) within the word embedding space. In this space, words that frequently co-occur in similar contexts are positioned close to each other. For instance, if a media outlet predominantly features male scientists, the word “scientist” and related male-centric terms, such as “man” and “he” will frequently co-occur. Consequently, these words will cluster near the word “scientist” in the embedding space, while female-related words occupy more distant positions. This enables us to evaluate the media outlet’s gender bias concerning the term “scientist” by comparing the embedding distances between “scientist” and words associated with both males and females. This approach aligns closely with the Semantic Differential theory in psychology (Osgood et al. 1957 ), which gauges an individual’s attitudes toward various concepts, objects, and events using bipolar scales constructed from adjectives with opposing semantics. In this study, to identify media bias from news articles, we first define two sets of words with opposite semantics for each topic to develop media bias evaluation scales. Then, we quantify media bias on each topic by calculating the embedding distance difference between a target word (e.g., scientist) and these two sets of words (e.g., female-related words and male-related words) in the word embedding space.

Compared with the bias in news articles, event selection bias is more obscure, as only events of interest to the media are reported in the final articles, while events deliberately ignored by the media remain invisible to the public. Similar to the co-occurrence relationship between words mentioned earlier, two media outlets that frequently select and report on the same events should exhibit similar biases in event selection, as two words that occur frequently in the same contexts have similar semantics. Therefore, we refer to Latent Semantic Analysis (LSA (Deerwester et al. 1990 )) and generate vector representation (i.e., media embedding) for each media via truncated singular value decomposition (Truncated SVD (Halko et al. 2011 )). Essentially, a media embedding encodes the distribution of the events that a media outlet tends to report on. Therefore, in the media embedding space, media outlets that often select and report on the same events will be close to each other due to similar distributions of the selected events. If a media outlet shows significant differences in such a distribution compared to other media outlets, we can conclude that it is biased in event selection. Inspired by this, we conduct clustering on the media embeddings to study how different media outlets differ in the distribution of selected events, i.e., the so-called event selection bias.

These two methodologies, designed for micro-level and macro-level analysis, share a fundamental similarity: both leverage data-driven embedding models to represent each word or media outlet as a distinctive vector within the embedding space and conduct further analysis based on these vectors. Therefore, in this study, we integrate both methodologies into a unified framework for media bias analysis. We aim to uncover media bias on a large scale and quantify it objectively on diverse topics. Our experiment results show that: (1) Different media outlets have different preferences for various news events, and those from the same country or organization tend to share more similar tastes. Besides, the occurrence of international hot events will lead to the convergence of different media outlets’ event selection. (2) Despite differences in media bias, some stereotypes, such as gender bias, are common among various media outlets. These findings align well with our empirical understanding, thus validating the effectiveness of our proposed framework.

Data and methods

The first dataset is the GDELT Mention Table, a product of the Google Jigsaw-backed GDELT project Footnote 5 . This project aims to monitor news reports from all over the world, including print, broadcast, and online sources, in over 100 languages. Each time an event is mentioned in a news report, a new row is added to the Mention Table (See Supplementary Information Tab. S1 for details). Given that different media outlets may report on the same event at varying times, the same event can appear in multiple rows of the table. While the fields GlobalEventID and EventTimeDate are globally unique attributes for each event, MentionSourceName and MentionTimeDate may differ. Based on the GlobalEventID and MentionSourceName fields in the Mention Table, we can count the number of times each media outlet has reported on each event, ultimately constructing a “media-event” matrix. In this matrix, the element at ( i , j ) denotes the number of times that media outlet j has reported on the event i in past reports.

As a global event database, GDELT collects a vast amount of global events and topics, encompassing news coverage worldwide. However, despite its widespread usage in many studies, there are still some noteworthy issues. Here, we highlight some of the issues to remind readers to use it more cautiously. Above all, while GDELT provides a vast amount of data from various sources, it cannot capture every event accurately. It relies on automated data collection methods, and this could result in certain events being missed. Furthermore, its algorithms for event extraction and categorization cannot always perfectly capture the nuanced context and meaning of each event, which might lead to potential misinterpretations.

The second dataset is built on MediaCloud Footnote 6 , an open-source platform for research on media ecosystems. MediaCloud’s API enables the querying of news article URLs for a given media outlet, which can then be retrieved using a web crawler. In this study, we have collected more than 1.2 million news articles from 12 mainstream US media outlets in 2016-2021 via MediaCloud’s API (See Supplementary Information Tab. S2 for details).

Media bias estimation by media embedding

Latent Semantic Analysis (LSA (Deerwester et al. 1990 )) is a well-established technique for uncovering the topic-based semantic relationships between text documents and words. By performing truncated singular value decomposition (Truncated SVD (Halko et al. 2011 )) on a “document-word” matrix, LSA can effectively capture the topics discussed in a corpus of text documents. This is accomplished by representing documents and words as vectors in a high-dimensional embedding space, where the similarity between vectors reflects the similarity of the topics they represent. In this study, we apply this idea to media bias analysis by likening media and events to documents and words, respectively. By constructing a “media-event” matrix and performing Truncated SVD, we can uncover the underlying topics driving the media coverage of specific events. Our hypothesis posits that media outlets mentioning certain events more frequently are more likely to exhibit a biased focus on the topics related to those events. Therefore, media outlets sharing similar topic tastes during event selection will be close to each other in the embedding space, which provides a good opportunity to shed light on the media’s selection bias.

The generation procedures for media embeddings are shown in Supplementary Information Fig. S1 . First, a “media-event” matrix denoted as A m × n is constructed based on the GDELT Mention Table, where m and n represent the total number of media outlets and events, respectively. Each entry A i , j represents the number of times that media i has reported on event j . Subsequently, Truncated SVD is performed on the matrix A m × n , which results in three matrices: U m × k , Σ k × k and ${V}_{n\times k}^{T}$ . The product of Σ k × k and ${V}_{n\times k}^{T}$ is represented by E k × n . Each column of E k × n corresponds to a k -dimensional vector representation for a specific media outlet, i.e., a media embedding. Specifically, the decomposition of matrix A m × n can be formulated as follows:

Equation( 1 ) defines the complete singular value decomposition of A m × n . Both ${U}_{m\times m}^{0}$ and ${({V}_{n\times n}^{0})}^{T}$ are orthogonal matrices. ${{{\Sigma }}}_{m\times n}^{0}$ is a m × n diagonal matrix whose diagonal elements are non-negative singular values of the matrix A m × n in descending order. Equation( 2 ) defines the truncated singular value decomposition (i.e., Truncated SVD) of A m × n . Based on the result of complete singular value decomposition, the part corresponding to the largest k singular values is equivalent to the result of Truncated SVD. Specifically, U m × k comprises the first k columns of the matrix ${U}_{m\times m}^{0}$ , while ${V}_{n\times k}^{T}$ comprises the first k rows of the matrix ${({V}_{n\times n}^{0})}^{T}$ . Additionally, the diagonal matrix Σ k × k is composed of the first k diagonal elements of ${{{\Sigma }}}_{m\times n}^{0}$ , representing the largest k singular values of A m × n . In particular, the media embedding model is defined as the product of the matrices Σ k × k and ${V}_{n\times k}^{T}$ , which has n k -dimensional media embeddings as follows:

To measure the similarity between two media embedding sets, we refer to Word Mover Distance (WMD (Kusner et al. 2015 )). WMD is designed to measure the dissimilarity between two text documents based on word embedding. Here, we subtract the optimal value of the original WMD objective function from 1 to convert the dissimilarity value into a normalized similarity score that ranges from 0 to 1. Specifically, the similarity between two media embedding sets is formulated as follows:

Let n denote the total number of media outlets, and s be an n -dimensional vector corresponding to the first media embedding set. For each i , the weight of media i in the embedding set is given by ${s}_{i}=\frac{1}{\sum_{k = 1}^{n}{t}_{i}}$ , where t i = 1 if media i is in the embedding set, and t i = 0 otherwise. Similarly, ${s}^{{\prime} }$ is another n -dimensional vector corresponding to the second media embedding set. The distance between media i and j is calculated using c ( i , j ) = ∥ e i − e j ∥ 2 , where e i and e j are the embedding representations of media i and j , respectively. The flow matrix T ∈ R n × n is used to determine how much media i in s travels to media j in ${s}^{{\prime} }$ . Specifically, T i , j ≥ 0 denotes the amount of flow from media i to media j .

Media bias estimation by word embedding

Word embedding models (Kenton and Toutanova, 2019 ; Le and Mikolov, 2014 ; Mikolov et al. 2013 ) are widely used in text-related tasks due to their ability to capture rich semantics of natural language. In this study, we regard media bias in news articles as a special type of semantic and capture it using Word2Vec (Le and Mikolov, 2014 ; Mikolov et al. 2013 ).

Supplementary Information Fig. S2 presents the process of building media corpora and training word embedding models to capture media bias. First, we reorganize the corpus for each media outlet by up-sampling to ensure that each media corpus contains the same number of news articles. The advantage of up-sampling is that it makes full use of the existing media corpus data, as opposed to discarding part of the data like down-sampling does. Second, we superimpose all 12 media corpora to construct a large base corpus and pre-train a Word2Vec model denoted as W b a s e based on it. Third, we fine-tune the same pre-trained model W b a s e using the specific corpus of each media outlet separately and get 12 fine-tuned models denoted as ${W}^{{m}_{i}}$ ( i = 1, 2, . . . 12).

In particular, the main objective of reorganizing the original corpora is to ensure that each corpus equivalently contributes to the pre-training process, in case a large corpus from certain media dominates the pre-trained model. As shown in Supplementary Information Tab. S2 , the largest corpus in 2016-2021 is from USA Today, which contains 295,518 news articles. Therefore, we can reorganize the other 11 media corpora by up-sampling to ensure that each of the 12 corpora has 295,518 articles. For example, as for NPR’s initial corpus, which has 14,654 news articles, we first repeatedly superimpose 295, 518//14, 654 = 20 times to get 293,080 articles and then randomly sample 295, 518%14, 654 = 2, 438 from the initial 14,654 articles as a supplement. Finally, we can get a reorganized NPR corpus with 295,518 articles.

Semantic Differential is a psychological technique proposed by (Osgood et al. 1957 ) to measure people’s psychological attitudes toward a given conceptual object. In the Semantic Differential theory, a given object’s semantic attributes can be evaluated in multiple dimensions. Each dimension consists of two poles corresponding to a pair of adjectives with opposite semantics (i.e., antonym pairs). The position interval between the poles of each dimension is divided into seven equally-sized parts. Then, given the object, respondents are asked to choose one of the seven parts in each dimension. The closer the position is to a pole, the closer the respondent believes the object is semantically related to the corresponding adjective. Supplementary Information Fig. S3 provides an example of Semantic Differential.

Constructing evaluation dimensions using antonym pairs in Semantic Differential is a reliable idea that aligns with how people generally evaluate things. For example, when imagining the gender-related characteristics of an occupation (e.g., nurse), individuals usually weigh between “man” and “woman”, both of which are antonyms regarding gender. Likewise, when it comes to giving an impression of the income level of the Asian race, people tend to weigh between “rich” (high income) and “poor” (low income), which are antonyms related to income. Based on such consistency, we can naturally apply Semantic Differential to measure a media outlet’s attitudes towards different entities and concepts, i.e., media bias.

Specifically, given a media m , a topic T (e.g., gender) and two semantically opposite topic word sets $P={\{{p}_{i}\}}_{i = 1}^{{K}_{1}}$ and $\neg P={\{\neg {p}_{i}\}}_{i = 1}^{{K}_{2}}$ about topic T , media m ’s bias towards the target x can be defined as:

Here, K 1 and K 2 denote the number of words in topic word sets P and ¬ P , respectively. W m represents the word embedding model obtained by fine-tuning W b a s e using the specific corpus of media m . $\overrightarrow{{W}_{x}^{m}}$ is the embedding representation of the word x in W m . S i m is a similarity function used to measure the similarity between two vectors (i.e., word embeddings). In practice, we employ the cosine similarity function, which is commonly used in the field of natural language processing. In particular, equation( 5 ) calculates the difference of average similarities between the target word x and two semantically opposite topic word sets, namely P and ¬ P . Similar to the antonym pairs in Semantic Differential, such two topic word sets are used to construct the evaluation scale of media bias. In practice, to ensure the stability of the results, we have repeated this experiment five times, each time with a different random seed for up-sampling. Therefore, the final results shown in Fig. 4 are the average bias values for each topic.

The idea of recovering media bias by embedding methods

We first analyzed media bias from the aspect of event selection to study which topics a media outlet tends to focus on or ignore. Based on the GDELT database, we constructed a large “media-event" matrix that records the times each media outlet mentioned each event in news reports from February to April 2022. To extract media bias information, we referred to the idea of Latent Semantic Analysis (Deerwester et al. 1990 ) and performed Truncated SVD (Halko et al. 2011 ) on this matrix to generate vector representation (i.e., media embedding) for each media outlet (See Methods for details). Specifically, outlets with similar event selection bias (i.e., outlets that often report on events of similar topics) will have similar media embeddings. Such a bias encoded in the vector representation of each outlet is exactly the first type of media bias we aim to study.

Then, we analyzed media bias in news articles to investigate the value judgments and attitudes conveyed by media through their news articles. We collected more than 1.2 million news articles from 12 mainstream US news outlets, spanning from January 2016 to December 2021, via MediaCloud’s API. To identify media bias from each outlet’s corpus, we performed three sequential steps: (1) Pre-train a Word2Vec word embedding model based on all outlets’ corpora. (2) Fine-tune the pre-trained model by using the specific corpus of each outlet separately and obtain 12 fine-tuned models corresponding to the 12 outlets. (3) Quantify each outlet’s bias based on the corresponding fine-tuned model, combined with the idea of Semantic Differential, i.e., measuring the embedding similarities between the target words and two sets of topic words with opposite semantics (See Methods for details). An example of using Semantic Differential (Osgood et al. 1957 ) to quantify media bias is shown in Supplementary Information Fig. S4 .

Media show significant clustering due to their regions and organizations

In this experiment, we aimed to capture and analyze the event selection bias of different media outlets based on the proposed media embedding methodology. To achieve a comprehensive analysis, we selected 247 media outlets from 8 countries ( Supplementary Information Tab. S6) , including the United States, the United Kingdom, Canada, Australia, Ireland, and New Zealand-six English-speaking nations with India and China, two populous countries. For each country, we chose media outlets that were the most active during February-April 2022, with media activity measured by the quantity of news reports. We then generated embedding representations for each media outlet via Truncated SVD and performed K-means clustering (Lloyd, 1982 ; MacQueen, 1967 ) on the obtained media embedding representations (with K = 10) for further analysis. Details of the experiment are presented in the first section of the supplementary Information. Figure 2 visualizes the clustering results.

There are 247 media outlets from 8 countries: Canada (CA), Ireland (IE), United Kingdom (UK), China (CN), United States (US), India (IN), Australia (AU), and New Zealand (NZ). Each circle in the visualization represents a media outlet, with its color indicating the cluster it belongs to, and its diameter proportional to the number of events reported by the outlet between February and April 2022. The text in each circle represents the name or abbreviation of a media outlet (See Supplementary Information Tab. S6 for details). The results indicate that media outlets from the same country tend to be grouped together in clusters. Moreover, the majority of media outlets in the Fox series form a distinct cluster, indicating a high degree of similarity in their event selection bias.

First, we find that media outlets from different countries tend to form distinct clusters, signifying the regional nature of media bias. Specifically, we can interpret Fig. 2 from two different perspectives, and both come to this conclusion. On the one hand, most media outlets from the same country tend to appear in a limited number of clusters, which suggests that they share similar event selection bias. On the other hand, as we can see, media outlets in the same cluster mostly come from the same country, indicating that media exhibiting similar event selection bias tends to be from the same country. In our view, differences in geographical location lead to diverse initial event information accessibility for media outlets from different regions, thus shaping the content they choose to report.

Besides, we observe an intriguing pattern where the Associated Press (AP) and Reuters, despite their geographical separation, share similar event selection biases as they are clustered together. This abnormal phenomenon could be attributed to their status as international media outlets, which enables them to cover various global events, thus leading to extensive overlapping news coverage. In addition, 16 out of the 21 Fox series media outlets form a distinct cluster on their own, suggesting that a media outlet’s bias is strongly associated with the organization it belongs to. After all, media outlets within the same organization often tend to prioritize or overlook specific events due to shared positions, interests, and other influencing factors.

International hot topics drive media bias to converge

Previous results have revealed a significant correlation between media bias and the location of a media outlet. Therefore, we conducted an experiment to further investigate the event selection biases of media outlets from 25 different countries. To achieve this, we gathered GDELT data spanning from February to April 2022 and created three “media-event” matrices on a monthly basis. We then subjected each month’s “media-event” matrix to the same processing steps: (1) generating an embedding representation for each media outlet through matrix decomposition, (2) obtaining the embedding representation of each media outlet that belongs to each country to construct a media embedding set, and (3) calculating the similarity between every two countries (i.e., each two media embedding sets) using Word Mover Distance (WMD (Kusner et al. 2015 )) as the similarity metric (See Methods for details). Figure 3 presents the changes in event selection bias similarity amongst media outlets from different countries between February and April 2022.

The horizontal axis in this figure represents the time axis, measured in months. Meanwhile, the vertical axis indicates the event selection similarity between Ukrainian media and media from other countries. Each circle represents a country, with the font inside it representing the corresponding country’s abbreviation (see details in Supplementary Information Tab. S3) . The size of a circle corresponds to the average event selection similarity between the media of a specific country and the media of all other countries. The color of the circle corresponds to the vertical axis scale. The blue dotted line’s ordinate represents the median similarity to Ukrainian media.

We find that the similarities between Ukraine and other countries peaked significantly in March 2022. This result aligns with the timeline of the Russia-Ukraine conflict: the conflict broke out around February 22, attracting media attention worldwide. In March, the conflict escalated, and the regional situation became increasingly tense, leading to even more media coverage worldwide. By April, the prolonged conflict had made the international media accustomed to it, resulting in a decline in media interest. Furthermore, we observed that the event selection biases of media outlets in both EG (Egypt) and CN (China) differed significantly from those of other countries. Given that both countries are not predominantly English-speaking, their English-language media outlets may have specific objectives such as promoting their national image and culture, which could influence and constrain the topics that a media outlet tends to cover.

Additionally, we observe that in March 2022, the country with the highest similarity to Ukraine was Russia, and in April, it was Poland. This change can be attributed to the evolving regional situation. In March, when the conflict broke out, media reports primarily focused on the warring parties, namely Russia and Ukraine. As the war continued, the impact of the war on Ukraine gradually became the focus of media coverage. For instance, the war led to the migration of a large number of Ukrainian citizens to nearby countries, among which Poland received the most citizens of Ukraine at that time.

Media shows diverse biases on different topics

In this experiment, we took 12 mainstream US news outlets as examples and conducted a quantitative media bias analysis on three typical topics (Fan and Gardent, 2022 ; Puglisi and Snyder Jr, 2015b ; Sun and Peng, 2021 ): Gender bias (about occupation); Political bias (about the American state); Income bias (about race & ethnicity). The topic words for each topic are listed in Supplementary Information Tab. S4 . These topic words are sourced from related literature (Caliskan et al. 2017 ), and search engines, along with the authors’ intuitive assessments.

Gender bias in terms of Occupation

In news coverage, media outlets may intentionally or unintentionally associate an occupation with a particular gender (e.g., stereotypes like police-man, nurse-woman). Such gender bias can subtly affect people’s attitudes towards different occupations and even impact employment fairness. To analyze gender biases in news coverage towards 8 common occupations (note that more occupations can be studied using the same methodology), we examined 12 mainstream US media outlets. As shown in Fig. 4 a, all these outlets tend to associate “teacher” and “nurse” with women. In contrast, when reporting on “police,” “driver,” “lawyer,” and “scientist,” most outlets show bias towards men. As for “director” and “photographer,” only slightly more than half of the outlets show bias towards men. Supplementary Information Tab. S5 shows the proportion of women in the eight occupations in America according to U.S. Bureau of Labor Statistics Footnote 7 . Women’s proportions in “teacher” and “nurse” dominate, while men’s in “police,” “driver,” and “lawyer” are significantly higher. Besides, among “directors,” “scientists,” and “photographers,” the proportions of women and men are about the same. Comparing the experiment results with USCB’s statistics, we find that these media outlets’ gender bias towards an occupation is highly consistent with the actual women (or men) ratio in the occupation. Such a phenomenon highlights the potential for media outlets to perpetuate and reinforce existing gender bias in society, emphasizing the need for increased awareness and attention to media bias. Note that we reorganized the corpus of each media outlet by up-sampling during the data preprocessing process, which introduced some randomness to the experiment results (See Methods for details). Therefore, we set five different random seeds for up-sampling and repeated the experiment mentioned above five times. A two-tailed t-test on the difference between the results shown in Fig. 4 a and the results of current repeated experiments showed no significant difference ( Supplementary Information Fig. S6) .

Each column corresponds to a media outlet, and each row corresponds to a target word which usually means an entity or concept in the news text. The color bar on the right describes the value range of the bias value, with each interval of the bias value corresponding to a different color. As the bias value changes from negative to positive, the corresponding color changes from purple to yellow. Because the range of bias values differs across each topic, the color bar of different topics can also vary. The color of each heatmap square corresponds to an interval in the color bar. Specifically, the square located in row i and column j represents the bias of media j when reporting on target i. a Gender bias about eight common occupations. b Income bias about four races or ethnicities. c Political bias about the top-10 “red state” (Wyoming, West Virginia, North Dakota, Oklahoma, Idaho, Arkansas, Kentucky, South Dakota, Alabama, Texas) and the top-10 “blue state” (Hawaii, Vermont, California, Maryland, Massachusetts, New York, Rhode Island, Washington, Connecticut, Illinois) according to the CPVI ranking (Ardehaly and Culotta, 2017 ). Limited by the page layout, only the top-8 results are shown here. Please refer to Supplementary Information Fig. S5 for the complete results.

Income bias in terms of Race and Ethnicity

Media coverage often discusses the income of different groups of people, including many races and ethnicities. Here, we aim to investigate whether the media outlets are biased in their income coverage, such as associating a specific race or ethnicity with rich or poor. To this end, we selected four US racial and ethnic groups as research subjects: Asian, African, Hispanic, and Latino. In line with previous studies (Grieco and Cassidy, 2015 ; Nerenz et al. 2009 ; Perez and Hirschman, 2009 ), we considered Asian and African as racial categories and Hispanic and Latino as ethnic categories. Referring to the income statistics from USCB Footnote 8 , we do not strictly distinguish these concepts and compare them together. As shown in Fig. 4 b, for the majority of media outlets, Asian is most frequently associated with the rich, with ESPN being the only exception. This anomalous finding may be attributed to ESPN’s position as a sports media, with a primary emphasis on sports that are particularly popular with Hispanic, Latino, and African-American audiences, such as soccer, basketball, and golf. Additionally, there is a significant disparity in the media’s coverage of income bias toward Africans, Hispanics, and Latinos. Specifically, the biases towards Hispanic and Latino populations are generally comparable, with both groups being portrayed as richer than African Americans in most media coverage. Referring to the aforementioned income statistics of the U.S. population, the income rankings of different races and ethnicities have remained stable from 1950 to 2020: Asians have consistently had the highest income, followed by Hispanics with the second-highest income, and African Americans with the lowest income (the income of Black Americans is used as an approximation for African Americans). It is worth noting that USCB considers Hispanic and Latino to be the same ethnicity, although there are some controversies surrounding this practice (Mora, 2014 ; Rodriguez, 2000 ). However, these controversies are not the concern of this work, so we use Hispanic income as an approximation of Latino income following USCB. Comparing our experiment results with USCB’s income statistics, we find that the media outlets’ income bias towards different races and ethnicities is roughly consistent with their actual income levels. A two-tailed t-test on the difference between the results shown in Fig. 4 b and the results of repeated experiments showed no significant difference ( Supplementary Information Fig. S7) .

Political bias in terms of Region

Numerous studies have shown that media outlets tend to publish politically biased news articles that support the political parties they favor while criticizing those they oppose (Lazaridou et al. 2020 ; Puglisi, 2011 ). For example, a report from the Red State described liberals as regressive leftists with mental health issues. Conversely, a story from Right Wing News reported that Obama’s administration was terrible (Lazaridou et al. 2020 ). Such political inclinations will hinder readers’ objective judgment of political events and affect their attitudes toward different political parties. Therefore, we analyzed the political biases of 12 mainstream US media outlets when talking about different US states, aiming to increase public awareness of such biases in news coverage. As shown in Fig. 4 c, in the reports of these media outlets, most red states lean Republican, while most blue states lean Democrat. In particular, some blue states also show a leaning toward Republicans, such as Hawaii and Maryland. Such an abnormal phenomenon can be attributed to the source of the corpus data used in this study. The corpus data, which was used to train word embedding models, spans from January 2016 to December 2021. During this period, the Republican Party was in power, with Trump serving as president from January 2017 to January 2021. Thus, the majority of the data was collected during the Republican administration. We suggest that Trump’s presidency resulted in increased media coverage of the Republican Party, thus causing some blue states to be associated more frequently with Republicans in news reports. A two-tailed t-test on the difference between the results shown in Fig. 4 c and the results of repeated experiments showed no significant difference ( Supplementary Information Fig. S8 and Fig. S9) .

Media logic and news evaluation are two important concepts in social science. The former refers to the rules, conventions, and strategies that the media follow in the production, dissemination, and reception of information, reflecting the media’s organizational structure, commercial interests, and socio-cultural background (Altheide, 2015 ). The latter refers to the systematic analysis of the quality, effectiveness, and impact of news reports, involving multiple criteria and dimensions such as truthfulness, accuracy, fairness, balance, objectivity, diversity, etc. When studying media bias issues, media logic provides a framework for understanding the rules and patterns of media operations, while news evaluation helps identify and analyze potential biases in media reports. For example, to study media’s political bias, (D’heer, 2018 ; Esser and Strömbäck, 2014 ) compare the frameworks, languages, and perspectives used by traditional news media and social media in reporting political elections, so as to understand the impact of these differences on voters’ attitudes and behaviors. However, in spite of the progress, these methods often rely on manual observation and interpretation, thus inefficient and susceptible to human bias and errors.

In this work, we propose an automated media bias analysis framework that enables us to uncover media bias on a large scale. To carry out this study, we amassed an extensive dataset, comprising over 8 million event records and 1.2 million news articles from a diverse range of media outlets (see details of the data collection process in Methods). Our research delves into media bias from two distinct yet highly pertinent perspectives. From the macro perspective, we aim to uncover the event selection bias of each media outlet, i.e., which types of events a media outlet tends to report on. From the micro perspective, our goal is to quantify the bias of each media outlet in wording and sentence construction when composing news articles about the selected events. The experimental results align well with our existing knowledge and relevant statistical data, indicating the effectiveness of embedding methods in capturing the characteristics of media bias. The methodology we employed is unified and intuitive and follows a basic idea. First, we train embedding models using real-world data to capture and encode media bias. At this step, based on the characteristics of different types of media bias, we choose appropriate embedding methods to model them respectively (Deerwester et al. 1990 ; Le and Mikolov, 2014 ; Mikolov et al. 2013 ). Then, we utilize various methods, including cluster analysis (Lloyd, 1982 ; MacQueen, 1967 ), similarity calculation (Kusner et al. 2015 ), and semantic differential (Osgood et al. 1957 ), to extract media bias information from the obtained embedding models.

To capture the event selection biases of different media outlets, we employ Truncated SVD (Halko et al. 2011 ) on the “media-event” matrix to generate media embeddings. Truncated SVD is a widely used technique in NLP. In particular, LSA (Deerwester et al. 1990 ) applies Truncated SVD to the “document-word” matrix to capture the underlying topic-based semantic relationships between text documents and words. LSA assumes that a document tends to use relevant words when it talks about a particular topic and obtains the vector representation for each document in a latent topic space, where documents talking about similar topics are located near each other. By analogizing media outlets and events with documents and words, we can naturally apply Truncate SVD to explore media bias in the event selection process. Specifically, we assume that there are underlying topics when considering a media outlet’s event selection bias. If a media focuses on a topic, it will tend to report events related to that topic and otherwise ignore them. Therefore, media outlets sharing similar event selection biases (i.e., tend to report events about similar topics) will be close to each other in the latent topic space, which provides a good opportunity for us to study media bias (See Methods and Results for details).

When describing something, relevant contexts must be considered. For instance, positive and negative impressions are conveyed through the use of context words such as “diligent” and “lazy”, respectively. Similarly, a media outlet’s attitude towards something is reflected in the news context in which it is presented. Here, we study the association between each target and its news contexts based on the co-occurrence relationship between words. Our underlying assumption is that frequently co-occurring words are strongly associated, which aligns with the idea of word embedding models (Kenton and Toutanova, 2019 ; Le and Mikolov, 2014 ; Mikolov et al. 2013 ), where the embeddings of frequently co-occurring words are relatively similar. For example, suppose that in the corpus of media M, the word “scientist” often co-occurs with female-related words (e.g., “woman” and “she”, etc.) but rarely with those male-related words. Then, the semantic similarities of “scientist” with female-related words should be much higher than those of male-related words in the word embedding model. Therefore, we can conclude that media M’s reports on scientists are biased towards women.

According to the theory of Semantic Differential (Osgood et al. 1957 ), the difference in semantic similarities between “scientist” and female-related words versus male-related words can serve as an estimation of media M’s gender bias. Since we have kept all settings (e.g., corpus size, starting point for model fine-tuning, etc.) the same when training word embedding models for different media outlets, the estimated bias values can be interpreted as absolute ones within the same reference system. In other words, the estimated bias values for different media outlets are directly comparable in this study, with a value of 0 denoting unbiased and a value closer to 1 or -1 indicating a more pronounced bias.

We notice that there has been literature investigating the choice of events/topics and words/frames to measure media bias, such as partisan and ideological biases (Gentzkow et al. 2015 ; Puglisi and Snyder Jr, 2015b ). However, our approach not only considers bias related to the selective reporting of events (using event embedding) but also studies biased wording in news texts (using word embedding). While the former focuses on the macro level, the latter examines the micro level. These two perspectives are distinct yet highly relevant, but previous studies often only consider one of them. For the choice of events/topics, our approach allows us to explore how they change over time. For example, we can analyze the time-changing similarities between media outlets from different countries, as shown in Fig. 3 . For the choice of words/frames, prior work has either analyzed specific biases based on the frequency of particular words (Gentzkow and Shapiro, 2010 ; Gentzkow et al. 2006 ), which fails to capture deeper semantics in media language or analyzed specific biases by merely aggregating the analysis results for every single article in the corpus (e.g., calculating the sentiment (Gentzkow et al. 2006 ; Lott Jr and Hassett, 2014 ; Soroka, 2012 ) of each article or its similarity with certain authorship (Gentzkow and Shapiro, 2010 ; Groseclose and Milyo, 2005 ), then summing them up as the final bias value), without considering the relationships between different articles, thus lacking a holistic nature. In contrast, our method, based on word embeddings (Le and Mikolov, 2014 ; Mikolov et al. 2013 ), directly models the semantic associations between all words and entities in the corpus with a neural network, offering advantages in capturing both semantic meaning and holistic nature. Specially, we not only utilize word embedding techniques but also integrate them with appropriate psychological/sociological theories, such as the Semantic Differential theory and the Cognitive Miser theory. These theories endow our approach with better interpretability. In addition, the method we propose is a generalizable framework for studying media bias using embedding techniques. While this study has focused on validating its effectiveness with specific types of media bias, it can actually be applied to a broader range of media bias research. We will expand the application of this framework in future work.

As mentioned above, our proposed framework examines media bias from two distinct but highly relevant perspectives. Here, taking the significant Russia-Ukraine conflict event as an example, we will demonstrate how these two perspectives contribute to providing researchers and the public with a more comprehensive and objective assessment of media bias. For instance, we can gather relevant news articles and event reporting records about the ongoing Russia-Ukraine conflict from various media outlets worldwide and generate media and word embedding models. Then, according to the embedding similarities of different media outlets, we can judge which types of events each media outlet tends to report and select some media that tend to report on different events. By synthesizing the news reports of the selected media, we can gain a more comprehensive understanding of the conflict instead of being limited to the information selectively provided by a few media. Besides, based on the word embedding model and the bias estimation method based on Semantic Differential, we can objectively judge each media’s attitude towards Russia and Ukraine (e.g., whether a media tends to use positive or negative words to describe either party). Once a news outlet is detected as apparently biased, we should read its articles more carefully to avoid being misled.

In the end, despite the advantages of our framework, there are still some shortcomings that need improvement. First, while the media embeddings generated based on matrix decomposition have successfully captured media bias in the event selection process, interpreting these continuous numerical vectors directly can be challenging. We hope that future work will enable the media embedding to directly explain what a topic exactly means and which topics a media outlet is most interested in, thus helping us understand media bias better. Second, since there is no absolute, independent ground truth on which events have occurred and should have been covered, the aforementioned media selection bias, strictly speaking, should be understood as relative topic coverage, which is a narrower notion. Third, for topics involving more complex semantic relationships, estimating media bias using scales based on antonym pairs and the Semantic Differential theory may not be feasible, which needs further investigation in the future.

Data availability

The data that support the findings of this study are available at https://github.com/CGCL-codes/media-bias .

Code availability

The code that supports the findings of this study is also available at https://github.com/CGCL-codes/media-bias .

https://fair.org/home/when-both-sides-are-covered-in-verizon-strike-bosses-side-is-heard-more/ .

These views were extracted from reports by some mainstream US media outlets in 2022 when the Democratic Party (left-wing) was in power.

https://www.nytimes.com/2022/09/26/world/middleeast/women-iran-protests-hijab.html .

https://www.nytimes.com/2014/08/08/world/asia/uighurs-veils-a-protest-against-chinas-curbs.html .

https://www.gdeltproject.org/ .

https://mediacloud.org/ .

https://www.bls.gov/cps/cpsaat11.htm .

https://www.census.gov/content/dam/Census/library/publications/2021/demo/p60-273.pdf .

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Acknowledgements

The work is supported by the National Natural Science Foundation of China (No. 62127808).

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HH: conceptualization, writing-review & editing, supervision; HZ: software, writing-original draft, data curation; WSL: software; HG and HJ: resources; BL: methodology, writing-review & editing, supervision.

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Huang, H., Zhu, H., Liu, W. et al. Uncovering the essence of diverse media biases from the semantic embedding space. Humanit Soc Sci Commun 11 , 656 (2024). https://doi.org/10.1057/s41599-024-03143-w

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Special Issue: Propaganda

This essay was published as part of the Special Issue “Propaganda Analysis Revisited”, guest-edited by Dr. A. J. Bauer (Assistant Professor, Department of Journalism and Creative Media, University of Alabama) and Dr. Anthony Nadler (Associate Professor, Department of Communication and Media Studies, Ursinus College).

Propaganda, misinformation, and histories of media techniques

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This essay argues that the recent scholarship on misinformation and fake news suffers from a lack of historical contextualization. The fact that misinformation scholarship has, by and large, failed to engage with the history of propaganda and with how propaganda has been studied by media and communication researchers is an empirical detriment to it, and serves to make the solutions and remedies to misinformation harder to articulate because the actual problem they are trying to solve is unclear.

School of Media and Communication, University of Leeds, UK

Introduction

Propaganda has a history and so does research on it. In other words, the mechanisms and methods through which media scholars have sought to understand propaganda—or misinformation, or disinformation, or fake news, or whatever you would like to call it—are themselves historically embedded and carry with them underlying notions of power and causality. To summarize the already quite truncated argument below, the larger conceptual frameworks for understanding information that is understood as “pernicious” in some way can be grouped into four large categories: studies of propaganda, the analysis of ideology and its relationship to culture, notions of conspiracy theory, and finally, concepts of misinformation and its impact. The fact that misinformation scholarship generally proceeds without acknowledging these theoretical frameworks is an empirical detriment to it and serves to make the solutions and remedies to misinformation harder to articulate because the actual problem to be solved is unclear.

The following pages discuss each of these frameworks—propaganda, ideology, conspiracy, and misinformation—before returning to the stakes and implications of these arguments for future research on pernicious media content.

Propaganda and applied research

The most salient aspect of propaganda research is the fact that it is powerful in terms of resources while at the same time it is often intellectually derided, or at least regularly dismissed. Although there has been a left-wing tradition of propaganda research housed uneasily within the academy (Herman & Chomsky, 1988; Seldes & Seldes, 1943), this is not the primary way in which journalism or media messaging has been understood in many journalism schools or mainstream communications departments. This relates, of course, to the institutionalization of journalism and communication studies within the academic enterprise. Within this paradox, we see the greater paradox of communication research as both an applied and a disciplinary field. Propaganda is taken quite seriously by governments, the military, and the foreign service apparatus (Simpson, 1994); at the same time, it has occupied a tenuous conceptual place in most media studies and communications departments, with the dominant intellectual traditions embracing either a “limited effects” notion of what communication “does” or else more concerned with the more slippery concept of ideology (and on that, see more below). There is little doubt that the practical study of the power of messages and the field of communication research grew up together. Summarizing an initially revisionist line of research that has now become accepted within the historiography of the field, Nietzel notes that “from the very beginning, communication research was at least in part designed as an applied science, intended to deliver systematic knowledge that could be used for the business of government to the political authorities.” He adds, however, that

“this context also had its limits, for by the end of the decade, communication research had become established at American universities and lost much of its dependence on state funds. Furthermore, it had become increasingly clear that communication scientists could not necessarily deliver knowledge to the political authorities that could serve as a pattern for political acting (Simpson, 1994 pp. 88–89). From then on, politics and communication science parted ways. Many of the approaches and techniques which seemed innovative and even revolutionary in the 1940s and early 1950s, promising a magic key to managing propaganda activities and controlling public opinion, became routine fields of work, and institutions like the USIA carried out much of this kind of research themselves.” (Nietzel, 2016, p. 66)

It is important to note that this parting of ways did not mean that no one in the United States and the Soviet Union was studying propaganda. American government records document that, in inflation-adjusted terms, total funding for the United States Information Agency (USIA) rose from $1.2 billion in 1955 to $1.7 billion in 1999, shortly before its functions were absorbed into the United States Department of State. And this was dwarfed by Soviet spending, which spent more money jamming Western Radio transmissions alone than the United States did in its entire propaganda budget. Media effects research in the form of propaganda studies was a big and well-funded business. It was simply not treated as such within the traditional academy (Zollman, 2019). It is also important to note that this does not mean that no one in academia studies propaganda or the effect of government messages on willing or unwilling recipients, particularly in fields like health communication (also quite well-funded). These more academic studies, however, were tempered by the generally accepted fact that there existed no decontextualized, universal laws of communication that could render media messages easily useable by interested actors.

Ideology, economics, and false consciousness

If academics have been less interested than governments and health scientists in analyzing the role played by propaganda in the formation of public opinion, what has the academy worried about instead when it comes to the study of pernicious messages and their role in public life? Open dominant, deeply contested line of study has revolved around the concept of ideology. As defined by Raymond Williams in his wonderful Keywords , ideology refers to an interlocking set of ideas, beliefs, concepts, or philosophical principles that are naturalized, taken for granted, or regarded as self-evident by various segments of society. Three controversial and interrelated principles then follow. First, ideology—particularly in its Marxist version—carries with it the implication that these ideas are somehow deceptive or disassociated from what actually exists. “Ideology is then abstract and false thought, in a sense directly related to the original conservative use but with the alternative—knowledge of real material conditions and relationships—differently stated” (Williams, 1976). Second, in all versions of Marxism, ideology is related to economic conditions in some fashion, with material reality, the economics of a situation, usually dominant and helping give birth to ideological precepts. In common Marxist terminology, this is usually described as the relationship between the base (economics and material conditions) and the superstructure (the realm of concepts, culture, and ideas). Third and finally, it is possible that different segments of society will have different ideologies, differences that are based in part on their position within the class structure of that society.

Western Marxism in general (Anderson, 1976) and Antonio Gramsci in particular helped take these concepts and put them on the agenda of media and communications scholars by attaching more importance to “the superstructure” (and within it, media messages and cultural industries) than was the case in earlier Marxist thought. Journalism and “the media” thus play a major role in creating and maintaining ideology and thus perpetuating the deception that underlies ideological operations. In the study of the relationship between the media and ideology, “pernicious messages” obviously mean something different than they do in research on propaganda—a more structural, subtle, reinforcing, invisible, and materially dependent set of messages than is usually the case in propaganda analysis. Perhaps most importantly, little research on media and communication understands ideology in terms of “discrete falsehoods and erroneous belief,” preferring to focus on processes of deep structural misrecognition that serves dominant economic interests (Corner, 2001, p. 526). This obviously marks a difference in emphasis as compared to most propaganda research.

Much like in the study of propaganda, real-world developments have also had an impact on the academic analysis of media ideology. The collapse of communism in the 1980s and 1990s and the rise of neoliberal governance obviously has played a major role in these changes. Although only one amongst a great many debates about the status of ideology in a post-Marxist communications context, the exchange between Corner (2001, 2016) and Downey (2008; Downey et al., 2014) is useful for understanding how scholars have dealt with the relationship between large macro-economic and geopolitical changes in the world and fashions of research within the academy. Regardless of whether concepts of ideology are likely to return to fashion, any analysis of misinformation that is consonant with this tradition must keep in mind the relationship between class and culture, the outstanding and open question of “false consciousness,” and the key scholarly insight that ideological analysis is less concerned with false messages than it is with questions of structural misrecognition and the implications this might have for the maintenance of hegemony.

Postmodern conspiracy

Theorizing pernicious media content as a “conspiracy” theory is less common than either of the two perspectives discussed above. Certainly, conspiratorial media as an explanatory factor for political pathology has something of a post-Marxist (and indeed, postmodern) aura. Nevertheless, there was a period in the 1990s and early 2000s when some of the most interesting notions of conspiracy theories were analyzed in academic work, and it seems hard to deny that much of this literature would be relevant to the current emergence of the “QAnon” cult, the misinformation that is said to drive it, and other even more exotic notions of elites conspiring against the public.

Frederic Jameson has penned remarks on conspiracy theory that represent the starting point for much current writing on the conspiratorial mindset, although an earlier and interrelated vein of scholarship can be found in the work of American writers such as Hofstadter (1964) and Rogin (1986). “Conspiracy is the poor person’s cognitive mapping in the postmodern age,” Jameson writes, “it is a degraded figure of the total logic of late capital, a desperate attempt to represent the latter’s system” (Jameson, 1991). If “postmodernism,” in Jameson’s terms, is marked by a skepticism toward metanarratives, then conspiracy theory is the only narrative system available to explain the various deformations of the capitalist system. As Horn and Rabinach put it:

“The broad interest taken by cultural studies in popular conspiracy theories mostly adopted Jameson’s view and regards them as the wrong answers to the right questions. Showing the symptoms of disorientation and loss of social transparency, conspiracy theorists are seen as the disenfranchised “poor in spirit,” who, for lack of a real understanding of the world they live in, come up with paranoid systems of world explanation.” (Horn & Rabinach, 2008)

Other thinkers, many of them operating from a perch within media studies and communications departments, have tried to take conspiracy theories more seriously (Bratich, 2008; Fenster, 2008; Pratt, 2003; Melley, 2008). The key question for all of these thinkers lies within the debate discussed in the previous section, the degree to which “real material interests” lie behind systems of ideological mystification and whether audiences themselves bear any responsibility for their own predicament. In general, writers sympathetic to Jameson have tended to maintain a Marxist perspective in which conspiracy represents a pastiche of hegemonic overthrow, thus rendering it just another form of ideological false consciousness. Theorists less taken with Marxist categories see conspiracy as an entirely rational (though incorrect) response to conditions of late modernity or even as potentially liberatory. Writers emphasizing that pernicious media content tends to fuel a conspiratorial mindset often emphasize the mediated aspects of information rather than the economics that lie behind these mediations. Both ideological analysis and academic writings on conspiracy theory argue that there is a gap between “what seems to be going on” and “what is actually going on,” and that this gap is maintained and widened by pernicious media messages. Research on ideology tends to see the purpose of pernicious media content as having an ultimately material source that is rooted in “real interests,” while research on conspiracies plays down these class aspects and questions whether any real interests exist that go beyond the exercise of political power.

The needs of informationally ill communities

The current thinking in misinformation studies owes something to all these approaches. But it owes an even more profound debt to two perspectives on information and journalism that emerged in the early 2000s, both of which are indebted to an “ecosystemic” perspective on information flows. One perspective sees information organizations and their audiences as approximating a natural ecosystem, in which different media providers contribute equally to the health of an information environment, which then leads to healthy citizens. The second perspective analyzes the flows of messages as they travel across an information environment, with messages becoming reshaped and distorted as they travel across an information network.

Both of these perspectives owe a debt to the notion of the “informational citizen” that was popular around the turn of the century and that is best represented by the 2009 Knight Foundation report The Information Needs of Communities (Knight Foundation, 2009). This report pioneered the idea that communities were informational communities whose political health depended in large part on the quality of information these communities ingested. Additional reports by The Knight Foundation, the Pew Foundation, and this author (Anderson, 2010) looked at how messages circulated across these communities, and how their transformation impacted community health.

It is a short step from these ecosystemic notions to a view of misinformation that sees it as a pollutant or even a virus (Anderson, 2020), one whose presence in a community turns it toward sickness or even political derangement. My argument here is that the current misinformation perspective owes less to its predecessors (with one key exception that I will discuss below) and more to concepts of information that were common at the turn of the century. The major difference between the concept of misinformation and earlier notions of informationally healthy citizens lies in the fact that the normative standard by which health is understood within information studies is crypto-normative. Where writings about journalism and ecosystemic health were openly liberal in nature and embraced notions of a rational, autonomous citizenry who just needed the right inputs in order to produce the right outputs, misinformation studies has a tendency to embrace liberal behavioralism without embracing a liberal political theory. What the political theory of misinformation studies is, in the end, deeply unclear.

I wrote earlier that misinformation studies owed more to notions of journalism from the turn of the century than it did to earlier traditions of theorizing. There is one exception to this, however. Misinformation studies, like propaganda analysis, is a radically de-structured notion of what information does. Buried within analysis of pernicious information there is

“A powerful cultural contradiction—the need to understand and explain social influence versus a rigid intolerance of the sociological and Marxist perspectives that could provide the theoretical basis for such an understanding. Brainwashing, after all, is ultimately a theory of ideology in the crude Marxian sense of “false consciousness.” Yet the concept of brainwashing was the brainchild of thinkers profoundly hostile to Marxism not only to its economic assumptions but also to its emphasis on structural, rather than individual, causality.” (Melley, 2008, p. 149)

For misinformation studies to grow in such a way that allows it to take its place among important academic theories of media and communication, several things must be done. The field needs to be more conscious of its own history, particularly its historical conceptual predecessors. It needs to more deeply interrogate its informational-agentic concept of what pernicious media content does, and perhaps find room in its arsenal for Marxist notions of hegemony or poststructuralist concepts of conspiracy. Finally, it needs to more openly advance its normative agenda, and indeed, take a normative position on what a good information environment would look like from the point of view of political theory. If this environment is a liberal one, so be it. But this position needs to be stated clearly.

Of course, misinformation studies need not worry about its academic bona fides at all. As the opening pages of this Commentary have shown, propaganda research was only briefly taken seriously as an important academic field. This did not stop it from being funded by the U.S. government to the tune of 1.5 billion dollars a year. While it is unlikely that media research will ever see that kind of investment again, at least by an American government, let’s not forget that geopolitical Great Power conflict has not disappeared in the four years that Donald Trump was the American president. Powerful state forces in Western society will have their own needs, and their own demands, for misinformation research. It is up to the scholarly community to decide how they will react to these temptations.

Mainstream Media
/ Propaganda

Cite this Essay

Anderson, C. W. (2021). Propaganda, misinformation, and histories of media techniques. Harvard Kennedy School (HKS) Misinformation Review . https://doi.org/10.37016/mr-2020-64

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More Americans now see the media’s influence growing compared with a year ago

Americans are now more likely to say the media are growing than declining in influence

Americans’ views about the influence of the media in the country have shifted dramatically over the course of a year in which there was much discussion about the news media’s role during the election and post-election coverage , the COVID-19 pandemic and protests about racial justice . More Americans now say that news organizations are gaining influence than say their influence is waning, a stark contrast to just one year ago when the reverse was true.

When Americans were asked to evaluate the media’s standing in the nation, about four-in-ten (41%) say news organizations are growing in their influence, somewhat higher than the one-third (33%) who say their influence is declining, according to a Pew Research Center survey conducted March 8-14, 2021. The remaining one-quarter of U.S. adults say they are neither growing nor declining in influence.

To examine Americans’ views about the influence of the news media, Pew Research Center surveyed 12,045 U.S. adults from March 8 to 14, 2021. Everyone who completed the 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 . See here to read more about the questions used for this analysis and the methodology .

This is the latest report in Pew Research Center’s ongoing investigation of the state of news, information and journalism in the digital age, a research program funded by The Pew Charitable Trusts, with generous support from the John S. and James L. Knight Foundation.

By comparison, Americans in early 2020 were far more likely to say the news media were declining in influence . Nearly half (48%) at that time said this, compared with far fewer (32%) who said news organizations were growing in influence.

The 2021 figures more closely resemble responses from 2011 – the next most recent time this was asked – and before, in that more Americans then said the news media were growing in influence than declining. Views could have shifted in the gap between 2011 and 2020, but if so, they have now shifted back. (It should be noted that prior to 2020, this question was asked on the phone instead of on the web.)

What’s more, this shift in views of the media’s influence in the country occurred among members of both political parties – and in the same direction.

Both Democrats and Republicans are more likely than last year to think the media are growing in influence

Republicans and Republican-leaning independents are about evenly split in whether they think news organizations are growing (40%) or declining in influence (41%). This is very different from a year ago, when Republicans were twice as likely to say their influence was declining than growing (56% vs. 28%).

And Democrats and Democratic leaners are now much more likely to say news organizations are growing (43%) than declining in influence (28%), while a year ago they were slightly more likely to say influence was declining (42% vs. 36% growing).

Overall, then, Republicans are still more likely than Democrats to say the news media are losing standing in the country, though the two groups are more on par in thinking that the media are increasing in their influence. (Democrats are somewhat more likely than Republicans to say news organizations are neither growing nor declining in influence – 29% vs. 19%.)

Americans who trust national news organizations are more likely to think news media influence is growing

Trust in media closely ties to whether its influence is seen as growing or declining. Those who have greater trust in national news organizations tend to be more likely to see the news media gaining influence, while those with low levels of trust are generally more likely to see it waning.

Americans who say they have a great deal of trust in the accuracy of political news from national news organizations are twice as likely to say the news media are growing than declining in influence (48% vs. 24%, respectively). Conversely, those who have no trust at all are much more likely to think that news organizations are declining (47% vs. 33% who say they are growing).

Most demographic groups more likely to say the news media growing than declining in influence

Black Americans are far more likely to think that the news media are growing in influence rather than declining (48% vs. 19%, respectively), as are Hispanic Americans though to a somewhat lesser degree. White Americans, on the other hand, are about evenly split in thinking the news media are growing or declining in influence (39% vs. 37%, respectively). And while men are about evenly split (39% growing vs. 38% declining), women are more likely to say news organizations are growing (43%) than declining (29%) in influence.

Note: Here are the questions used for this analysis, along with responses, and its methodology .

Media Attitudes
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Jeffrey Gottfried is an associate director focusing on internet and technology research at Pew Research Center .

Naomi Forman-Katz is a research analyst focusing on news and information research at Pew Research Center .

Americans’ Changing Relationship With Local News

Introducing the pew-knight initiative, 8 facts about black americans and the news, u.s. adults under 30 now trust information from social media almost as much as from national news outlets, u.s. journalists differ from the public in their views of ‘bothsidesism’ in journalism, most popular.

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ABOUT PEW RESEARCH CENTER Pew Research Center is a nonpartisan fact tank that informs the public about the issues, attitudes and trends shaping the world. It conducts public opinion polling, demographic research, media content analysis and other empirical social science research. Pew Research Center does not take policy positions. It is a subsidiary of The Pew Charitable Trusts .

80 Media Bias Essay Topic Ideas & Examples

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Media Bias and Democracy in India

By janani mohan.

June 28, 2021

This article was originally published in South Asian Voices.

As the COVID-19 pandemic rages out of control in India, many are rightly focusing on the content of stories on the death toll and months of lockdown. The lack of journalistic integrity behind some of the stories deepens this grim situation. In April, reports emerged that, at the request of the Indian government, Twitter censored 52 tweets criticizing the government’s handling of the pandemic. Meanwhile, pro-government TV channels blamed the farmers’ protests for limited oxygen supplies for COVID-19 patients, though supplies were actually scarce due to poor public health infrastructure. This reporting is not only misleading and traumatic to those affected by the pandemic, but also poses a major threat to India’s vibrant democracy.

Even before the pandemic, media bias in India existed across the largest newspapers throughout the country, and political forces shape this bias. For example, funds from the government are critical to many newspapers’ operations and budgets, and the current Bhartiya Janata Party (BJP) government has previously refused to advertise with newspapers that do not support its initiatives. This pressure leads media to endorse government policies, creating unbalanced reporting where media bias can affect political behavior in favor of the incumbent. Many media outlets enjoy a symbiotic relationship with the government, in turn receiving attention, funding, and prominence. These trends damage India’s democracy and also put journalists critical of the government in danger, threatening their right to physical safety.

Funds from the government are critical to many newspapers’ operations and budgets, and the current Bhartiya Janata Party (BJP) government has previously refused to advertise with newspapers that do not support its initiatives.

Media Bias in India

While the COVID-19 pandemic has exacerbated media bias in India, it is hardly a new phenomenon. A study of 30 Indian newspapers and 41 Indian TV channels with the largest viewership rates in the country confirms the existence of rampant media bias during a two-year period from 2017 to 2018. 1

The study relies on rating editorial articles that focus on religious, gender, and caste issues as either liberal, neutral, or conservative; and then compiling these scores by each newspaper to find the overall bias in each outlet. The results unsurprisingly and unfortunately show the consistent existence of media bias—for example, except for eight newspapers, the papers all express biases far from neutral. And this bias consistently correlates with viewers in India expressing similarly biased social, economic, and security attitudes.

What this suggests is either that biases in the media shape viewer attitudes or Indians are viewing outlets that align with their pre-existing views. Meanwhile, political parties capitalize on this bias to influence public attitudes and further their own power. The BJP spends almost USD $140 million on publicity per year, with 43 percent of this expenditure focusing specifically on print ads in newspapers. Government advertisements serve as a financial lever for influencing media content and public opinion. For example, during the year leading to the 2019 elections, newspapers that received more advertisement revenue from the BJP were likelier to espouse more conservative ideology and to have more conservative readers.

Bias versus Democracy

This ability of media bias to influence political support in India can contribute significantly to democratic backsliding by harming journalists, preventing freedom of expression and government accountability, and influencing voters. Media bias in itself causes democratic backsliding because the media neither holding the government accountable nor informing the public about policies that strengthen the incumbent’s power can increase authoritarian practices.

In addition, government efforts to constrain the media harms journalists, undemocratically violating citizens’ rights and physical safety. Freedom House rates India as only two on a four-point scale for whether there is a “free and independent media,” because of “attacks on press freedom…under the Modi government.” In fact, the government imprisoned several journalists in 2020 who reported critically on Prime Minister (PM) Narendra Modi’s response to the pandemic. The crackdown on journalists engendered an unsafe environment for free reporting, a feature of many authoritarian states.

A biased media also prevents citizens from receiving information that might be essential to public wellbeing by filtering information through a lens that supports government interests first. When the BJP cracked down on coverage of COVID-19 last year, journalists were unable to disseminate critical information to Indians. This included where migrants suffering from the sudden lockdown could receive necessities—information that could save lives. Notably, these crackdowns also meant an absence of reporting criticizing the government’s response to the pandemic. In a democratic society, a critical press is essential for holding the government accountable for its actions and motivating it to change its practices.

Media bias plays an influencing role at the voting booth as propaganda can skew voter decisions and perceptions of what is true.

Finally, media bias plays an influencing role at the voting booth as propaganda can skew voter decisions and perceptions of what is true. During India’s 2014 general elections, the BJP advertised more than the Congress Party and voters exposed to more media were likelier to vote for the BJP. To influence voters, media bias often utilizes inflammatory messaging to convince more people to vote, selective information to bias what voters believe about the efficacy of the candidates, and appeasement to convince voters that they will personally benefit from voting a certain way. For example, a TimesNow interview of PM Modi before the 2019 elections made it seem that Modi’s economic policies—widely criticized as ineffectual—were successful.

From Media Bias to Media Neutrality

Although government measures are exacerbating media bias, the media retains some agency and could work to limit the influence of politics on reporting. Currently, 36 percent of daily newspapers earn over half of their total income from the government of India and most major TV stations have owners who served as politicians themselves or who had family members in politics. Although it would be difficult to convince larger outlets to participate since they benefit from their government backing, smaller independent outlets can start this movement towards neutrality. Many small outlets already eschew government funding and report with less biased views. These publications in India therefore deserve more attention and more support to reduce media bias.

While India has some of the highest circulation of newspapers in the world, it also unfortunately has high media bias rates and one of the lowest press freedom rankings for democracies. This media bias can contribute to democratic backsliding and must be addressed by media outlets. Only then can media in India properly do its job—serving to inform, not influence the public.

The author would like to acknowledge Dr. Pradeep Chhibber, Pranav Gupta, and UC Berkeley for supporting her research measuring media bias in India. All perspectives in this article are her own.

This article was originally published in South Asian Voices.

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June 21, 2018

Biases Make People Vulnerable to Misinformation Spread by Social Media

Researchers have developed tools to study the cognitive, societal and algorithmic biases that help fake news spread

By Giovanni Luca Ciampaglia , Filippo Menczer & The Conversation US

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The following essay is reprinted with permission from The Conversation , an online publication covering the latest research.

Social media are among the primary sources of news in the U.S. and across the world. Yet users are exposed to content of questionable accuracy, including conspiracy theories , clickbait , hyperpartisan content , pseudo science and even fabricated “fake news” reports .

It’s not surprising that there’s so much disinformation published: Spam and online fraud are lucrative for criminals , and government and political propaganda yield both partisan and financial benefits . But the fact that low-credibility content spreads so quickly and easily suggests that people and the algorithms behind social media platforms are vulnerable to manipulation.

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Explaining the tools developed at the Observatory on Social Media.

Our research has identified three types of bias that make the social media ecosystem vulnerable to both intentional and accidental misinformation. That is why our Observatory on Social Media at Indiana University is building tools to help people become aware of these biases and protect themselves from outside influences designed to exploit them.

Bias in the brain

Cognitive biases originate in the way the brain processes the information that every person encounters every day. The brain can deal with only a finite amount of information, and too many incoming stimuli can cause information overload . That in itself has serious implications for the quality of information on social media. We have found that steep competition for users’ limited attention means that some ideas go viral despite their low quality —even when people prefer to share high-quality content.*

To avoid getting overwhelmed, the brain uses a number of tricks . These methods are usually effective, but may also become biases when applied in the wrong contexts.

One cognitive shortcut happens when a person is deciding whether to share a story that appears on their social media feed. People are very affected by the emotional connotations of a headline , even though that’s not a good indicator of an article’s accuracy. Much more important is who wrote the piece .

To counter this bias, and help people pay more attention to the source of a claim before sharing it, we developed Fakey , a mobile news literacy game (free on Android and iOS ) simulating a typical social media news feed, with a mix of news articles from mainstream and low-credibility sources. Players get more points for sharing news from reliable sources and flagging suspicious content for fact-checking. In the process, they learn to recognize signals of source credibility, such as hyperpartisan claims and emotionally charged headlines.

Bias in society

Another source of bias comes from society. When people connect directly with their peers, the social biases that guide their selection of friends come to influence the information they see.

In fact, in our research we have found that it is possible to determine the political leanings of a Twitter user by simply looking at the partisan preferences of their friends. Our analysis of the structure of these partisan communication networks found social networks are particularly efficient at disseminating information – accurate or not – when they are closely tied together and disconnected from other parts of society .

The tendency to evaluate information more favorably if it comes from within their own social circles creates “ echo chambers ” that are ripe for manipulation, either consciously or unintentionally. This helps explain why so many online conversations devolve into “us versus them” confrontations .

To study how the structure of online social networks makes users vulnerable to disinformation, we built Hoaxy , a system that tracks and visualizes the spread of content from low-credibility sources, and how it competes with fact-checking content. Our analysis of the data collected by Hoaxy during the 2016 U.S. presidential elections shows that Twitter accounts that shared misinformation were almost completely cut off from the corrections made by the fact-checkers.

When we drilled down on the misinformation-spreading accounts, we found a very dense core group of accounts retweeting each other almost exclusively – including several bots. The only times that fact-checking organizations were ever quoted or mentioned by the users in the misinformed group were when questioning their legitimacy or claiming the opposite of what they wrote.

Bias in the machine

The third group of biases arises directly from the algorithms used to determine what people see online. Both social media platforms and search engines employ them. These personalization technologies are designed to select only the most engaging and relevant content for each individual user. But in doing so, it may end up reinforcing the cognitive and social biases of users, thus making them even more vulnerable to manipulation.

For instance, the detailed advertising tools built into many social media platforms let disinformation campaigners exploit confirmation bias by tailoring messages to people who are already inclined to believe them.

Also, if a user often clicks on Facebook links from a particular news source, Facebook will tend to show that person more of that site’s content . This so-called “ filter bubble ” effect may isolate people from diverse perspectives, strengthening confirmation bias.

Our own research shows that social media platforms expose users to a less diverse set of sources than do non-social media sites like Wikipedia. Because this is at the level of a whole platform, not of a single user, we call this the homogeneity bias .

Another important ingredient of social media is information that is trending on the platform, according to what is getting the most clicks. We call this popularity bias , because we have found that an algorithm designed to promote popular content may negatively affect the overall quality of information on the platform. This also feeds into existing cognitive bias, reinforcing what appears to be popular irrespective of its quality.

All these algorithmic biases can be manipulated by social bots , computer programs that interact with humans through social media accounts. Most social bots, like Twitter’s Big Ben , are harmless. However, some conceal their real nature and are used for malicious intents, such as boosting disinformation or falsely creating the appearance of a grassroots movement , also called “astroturfing.” We found evidence of this type of manipulation in the run-up to the 2010 U.S. midterm election.

To study these manipulation strategies, we developed a tool to detect social bots called Botometer . Botometer uses machine learning to detect bot accounts, by inspecting thousands of different features of Twitter accounts, like the times of its posts, how often it tweets, and the accounts it follows and retweets. It is not perfect, but it has revealed that as many as 15 percent of Twitter accounts show signs of being bots .

Using Botometer in conjunction with Hoaxy, we analyzed the core of the misinformation network during the 2016 U.S. presidential campaign. We found many bots exploiting both the cognitive, confirmation and popularity biases of their victims and Twitter’s algorithmic biases.

These bots are able to construct filter bubbles around vulnerable users, feeding them false claims and misinformation. First, they can attract the attention of human users who support a particular candidate by tweeting that candidate’s hashtags or by mentioning and retweeting the person. Then the bots can amplify false claims smearing opponents by retweeting articles from low-credibility sources that match certain keywords. This activity also makes the algorithm highlight for other users false stories that are being shared widely.

Understanding complex vulnerabilities

Even as our research, and others’, shows how individuals, institutions and even entire societies can be manipulated on social media, there are many questions left to answer. It’s especially important to discover how these different biases interact with each other, potentially creating more complex vulnerabilities.

Tools like ours offer internet users more information about disinformation, and therefore some degree of protection from its harms. The solutions will not likely be only technological , though there will probably be some technical aspects to them. But they must take into account the cognitive and social aspects of the problem.

*Editor’s note: This article was updated on Jan. 10, 2019, to remove a link to a study that has been retracted. The text of the article is still accurate, and remains unchanged.

This article was originally published on The Conversation . Read the original article .

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AllSides Media Bias Chart

The AllSides Media Bias Chart™ helps you to easily identify different perspectives and political leanings in the news so you can get the full picture and think for yourself.

Knowing the political bias of media outlets allows you to consume a balanced news diet and avoid manipulation, misinformation, and fake news. Everyone is biased, but hidden media bias misleads and divides us. The AllSides Media Bias Chart™ is based on our full and growing list of over 1,400 media bias ratings . These ratings inform our balanced newsfeed .

The AllSides Media Bias Chart™ is more comprehensive in its methodology than any other media bias chart on the Web. While other media bias charts show you the subjective opinion of just one or a few people, our ratings are based on multipartisan, scientific analysis, including expert panels and surveys of thousands of everyday Americans.

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This chart does not rate accuracy or credibility. A publication can be accurate, yet biased. Learn why AllSides doesn't rate accuracy.

Unless otherwise noted, these bias ratings are based on online written content , not TV, radio, or broadcast content.

Here's how the AllSides Media Bias Chart™ differs from other media bias charts:

Data is gathered from many people across the political spectrum — not just one biased individual or a very small, elite group. We have a patent on rating bias and use multiple methodologies , not an algorithm. Our methods are : Blind Bias Surveys of Americans, Editorial Reviews by a multipartisan team of panelists who look for common types of media bias , independent reviews, and third party data.
Our research spans years — we started rating media bias back in 2012.
We give separate bias ratings for the news and opinion sections for some media outlets, giving you a more precise understanding.
Transparent methodology: we tell you how we arrived at the bias rating for each outlet. Search for any media outlet here.
We consider and review data and research conducted by third parties , like universities and other groups.
Your opinion matters: we take into account hundreds of thousands of community votes on our ratings. Votes don't determine our ratings, but are valuable feedback that may prompt more research. We know that a mixed group of experts and non-experts will provide a more accurate result, so we solicit and consider opinions of average people.
We don't rate accuracy — just bias. Our ratings help readers to understand that certain facts may be missing if they read only outlets from one side of the political spectrum.

Americans are more polarized than ever — if you’re like us, you see it in the news and on your social media feeds every day. Bias is natural, but hidden bias and fake news misleads and divides us. That’s why AllSides has rated the media bias of over 1,400 sources. and put it into a media bias chart. The AllSides Media Bias Chart™ shows the political bias of some of the most-read sources in America.

The outlets featured on the AllSides Media Bias Cart™ have varying degrees of influence. Read about whether conservative or liberal media outlets are more widely read .

Frequently Asked Questions about the AllSides Media Bias Chart

Why does the bias of a media outlet matter, how does allsides calculate media bias, how did allsides decide which media outlets to include on the chart, what do the bias ratings mean, does a center rating mean neutral, unbiased, and better, why are some media outlets on the chart twice, does allsides rate which outlets are most factual or accurate, where can i see past versions of the chart, where can i learn more, i disagree with your media bias ratings. where can i give you feedback.

News media, social media, and search engines have become so biased, politicized, and personalized that we are often stuck inside filter bubbles , where we’re only exposed to information and ideas we already agree with. When bias is hidden and we see only facts, information, and opinions that confirm our existing beliefs , a number of negative things happen: 1) we become extremely polarized as a nation as we misunderstand or hate the "the other side," believing they are extreme, hateful, or evil; 2) we become more likely to be manipulated into thinking, voting, or behaving a certain way; 3) we become limited in our ability to understand others, problem solve and compromise; 4) we become unable to find the truth.

It feels good to hear from people who think just like us, and media outlets have an incentive to be partisan — it helps them to earn ad revenue, especially if they use sensationalism and clickbait . But when we stay inside a filter bubble, we may miss important ideas and perspectives. The mission of AllSides is to free people from filter bubbles so they can better understand the world — and each other. Making media bias transparent helps us to easily identify different perspectives and expose ourselves to a variety of information so we can avoid being manipulated by partisan bias and fake news. This improves our country long-term, helping us to understand one another, solve problems, know the truth, and make better decisions.

Media bias has contributed to Americans becoming more politically polarized .

At AllSides, we reduce the one-sided information flow by providing balanced news from both liberal and conservative news sources, and over 1,400 media bias ratings . Our tools help you to better understand diverse perspectives and reduce harmful, hateful polarization in America. By making media bias transparent and consuming a balanced news diet, we can arm ourselves with a broader view — and find the truth for ourselves.

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Our media bias ratings are based on multi-partisan, scientific analysis. Our methodologies include Blind Bias Surveys of Americans, Editorial Reviews by a panel of experts trained to spot bias , independent reviews, third party data, and community feedback. Visit our Media Bias Rating Methodology page to learn more.

We consider multiple factors including how much traffic the source has according to Pew Research Center and Similarweb , and how many searches for the bias of that outlet land on AllSides.

We also include outlets that represent outlier perspectives. For example, Jacobin magazine is included because it represents socialist thought, while Reason magazine is included because it represents libertarian thought.

These are subjective judgements made by AllSides and people across the country. Learn our rough approximation for what the media bias ratings mean:

Left - Lean Left - Center - Lean Right - Right

Center doesn't mean better! A Center media bias rating does not mean the source is neutral, unbiased, or reasonable, just as Left and Right do not necessarily mean the source is extreme, wrong, or unreasonable. A Center bias rating simply means the source or writer rated does not predictably publish content that tilts toward either end of the political spectrum — conservative or liberal. A media outlet with a Center rating may omit important perspectives, or run individual articles that display bias, while not displaying a predictable bias. Center outlets can be difficult to determine, and there is rarely a perfect Center outlet: some of our outlets rated Center can be better thought of as Center-Left or Center-Right, something we clarify on individual source pages.

While it may be easy to think that we should only consume media from Center outlets, AllSides believes reading in the Center is not the answer. By reading only Center outlets, we may still encounter bias and omission of important issues and perspectives. For this reason, it is important to consume a balanced news diet across the political spectrum, and to read horizontally across the bias chart. Learn more about what an AllSides Media Bias Rating™ of Center rating means here.

We sometimes provide separate media bias ratings for a source’s news content and its opinion content. This is because some outlets, such as the Wall Street Journal and The New York Times , have a notable difference in bias between their news and opinion sections.

For example, on this chart you will see The New York Times Opinion is rated as a Left media bias, while the New York Times news is rated Lean Left .

When rating an opinion page, AllSides takes into account the outlet's editorial board and its individual opinion page writers. The editorial board’s bias is weighted, and affects the final bias rating by about 60%.

For example, the New York Times has a range of individual Opinion page writers, who have a range of biases. We rate the bias of commentators individually as much as possible. Yet The New York Times Editorial Board has a clear Left media bias. We take into account both the overall biases of the individual writers and the Editorial Board to arrive at a final bias rating of Left for the New York Times opinion section .

See how we provide individual bias ratings for New York Times opinion page writers here .

AllSides does not rate outlets based on accuracy or factual claims — this is a bias chart, not a credibility chart. It speaks to perspective only.

We don't rate accuracy because we don't assume we know the truth on all things. The left and right often strongly disagree on what is truth and what is fiction. Read more about why AllSides doesn't rate accuracy.

We disagree with the idea that the more left or right an outlet is, the less credibility it has. There’s nothing wrong with having bias or an opinion, but hidden bias misleads and divides us. Just because an outlet is credible doesn’t mean it isn’t biased ; likewise, just because an outlet is biased doesn’t mean it isn’t credible .

Learn more about past versions of the chart on our blog:

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Visit the AllSides Media Bias Ratings™ page and search for any outlet for a full summation of our research and how we arrived at the rating.

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You can vote on whether or not you agree with media bias ratings , contact us , or sign up to participate in our next Blind Bias Survey .

35 Media Bias Examples for Students

Media bias examples include ideological bias, gotcha journalism, negativity bias, and sensationalism. Real-life situations when they occur include when ski resorts spin snow reports to make them sound better, and when cable news shows like Fox and MSNBC overtly prefer one political party over another (republican and democrat, respectively).

No one is free of all bias. No one is perfectly objective. So, every book, research paper, and article (including this one) is bound to have some form of bias.

The media is capable of employing an array of techniques to modify news stories in favor of particular interests or groups.

While bias is usually seen as a bad thing, and good media outlets try to minimize it as much as possible, at times, it can also be seen as a good thing. For example, a reporter’s bias toward scholarly consensus or a local paper’s bias toward reporting on events relevant to local people makes sense.

Media Bias Definition

Media bias refers to the inherently subjective processes involved in the selection and curation of information presented within media. It can lead to incorrect, inaccurate, incomplete, misleading, misrepresented, or otherwise skewed reporting.

Media bias cannot be fully eliminated. This is because media neutrality has practical limitations, such as the near impossibility of reporting every single available story and fact, the requirement that selected facts must form a coherent narrative, and so on (Newton, 1996).

Types of Media Bias

In a broad sense, there are two main types of media bias .

Ideological bias reflects a news outlet’s desire to move the opinions of readers in a particular direction.
Spin bias reflects a news outlet’s attempt to create a memorable story (Mullainathan & Shleifer, 2002).

These two main types can be divided into many subcategories. The following list offers a more specific classification of different types of media bias:

Advertising bias occurs when stories are selected or slanted to please advertisers (Eberl et al., 2018).
Concision bias occurs when conciseness determines which stories are reported and which are ignored. News outlets often report views that can be summarized succinctly, thereby overshadowing views that are more unconventional, difficult to explain, and complex.
Confirmation bias occurs when media consumers tend to believe those stories, views, and research that confirms their current views and ignore everything else (Groseclose & Milyo, 2005).
Content bias occurs when two political parties are treated differently and news is biased towards one side (Entman, 2007).
Coverage bias occurs when the media chooses to report only negative news about one party or ideology (Eberl et al., 2017 & D’Alessio & Allen, 2000)
Decision-making bias occurs when the motivations, beliefs, and intentions of the journalists have an impact on what they write and how (Entman, 2007).
Demographic bias occurs when demographic factors, such as race, gender, social status, income, and so on are allowed to influence reporting (Ribeiro et al., 2018).
Gatekeeping bias occurs when stories are selected or dismissed on ideological grounds (D’Alessio & Allen, 2000). This is sometimes also referred to as agenda bias , selectivity bias (Hofstetter & Buss, 1978), or selection bias (Groeling, 2013). Such bias is often focused on political actors (Brandenburg, 2006).
Layout bias occurs when an article is placed in a section that is less read so that it becomes less important, or when an article is placed first so that more people read it. This can sometimes be called burying the lead .
Mainstream bias occurs when a news outlet only reports things that are safe to report and everyone else is reporting. By extension, the news outlet ignores stories and views that might offend the majority.
Partisan bias occurs when a news outlet tends to report in a way that serves a specific political party (Haselmayer et al., 2017).
Sensationalism bias occurs when the exceptional, the exciting, and the sensational are given more attention because it is rarer.
Statement bias occurs when media coverage is slanted in favor of or against specific actors or issues (D’Alessio & Allen, 2000). It is also known as tonality bias (Eberl et al., 2017) or presentation bias (Groeling, 2013).
Structural bias occurs when an actor or issue receives more or less favorable coverage as a result of newsworthiness instead of ideological decisions (Haselmayer et al., 2019 & van Dalen, 2012).
Distance bias occurs when a news agency gives more coverage to events physically closer to the news agency than elsewhere. For example, national media organizations like NBC may be unconsciously biased toward New York City news because that is where they’re located.
Negativity bias occurs because negative information tends to attract more attention and is remembered for a longer time, even if it’s disliked in the moment.
False balance bias occurs when a news agency attempts to appear balanced by presenting a news story as if the data is 50/50 on the topic, while the data may in fact show one perspective should objectively hold more weight. Climate change is the classic example.

Media Bias Examples

Ski resorts reporting on snowfall: Ski resorts are biased in how they spin snowfall reporting. They consistently report higher snowfall than official forecasts because they have a supply-driven interest in doing so (Raymond & Taylor, 2021).
Moral panic in the UK: Cohen (1964) famously explored UK media’s sensationalist reporting about youth subcultural groups as “delinquents”, causing panic among the general population that wasn’t representative of the subcultural groups’ true actions or impact on society.
Murdoch media in Australia: Former Prime Minister Kevin Rudd consistently reports on media bias in the Murdoch media, highlighting for example, that Murdoch’s papers have endorsed the conservative side of politics (ironically called the Liberals) in 24 out of 24 elections.
Fox and MSNBC: In the United States, Fox and MSNBC have niched down to report from a right- and left-wing bias, respectively.
Fog of war: During wartime, national news outlets tend to engage in overt bias against the enemy by reporting extensively on their war crimes while failing to report on their own war crimes.
Missing white woman syndrome: Sensationalism bias is evident in cases such as missing woman Gabby Petito . The argument of this type of bias is that media tends only to report on missing women when they are white, and neglect to make as much of a fuss about missing Indigenous women.
First-World Bias in Reporting on Natural Disasters: Scholars have found that news outlets tend to have bias toward reporting on first-world nations that have suffered natural disasters while under-reporting on natural disasters in developing nations, where they’re seen as not newsworthy (Aritenang, 2022; Berlemann & Thomas, 2018).
Overseas Reporting on US Politics: Sensationalism bias has an effect when non-US nations report on US politics. Unlike other nations’ politics, US politics is heavily reported worldwide. One major reason is that US politics tends to be bitterly fought and lends itself to sensational headlines.
Click baiting: Media outlets that have moved to a predominantly online focus, such as Forbes and Vice, are biased toward news reports that can be summed up by a sensational headline to ensure they get clicked – this is called “click baiting”.
Google rankings and mainstream research bias: Google has explicitly put in its site quality rater guidelines a preference for sites that report in ways that reflect “expert consensus”. While this may be seen as a positive way to use bias, it can also push potentially valid alternative perspectives and whistleblowers off the front page of search results.
False Balance on climate change: Researchers at Northwestern University have highlighted the prevalence of false balance reporting on climate change. They argue that 99% of scientists agree that it is man-made, yet often, news segments have one scientist arguing one side and another arguing another, giving the reporting a perception that it’s a 50-50 split in the scientific debate. In their estimation, an unbiased report would demonstrate the overwhelming amount of scientific evidence supporting one side over the other.
Negative Unemployment Reports: Garz found that media tend to over-report negative unemployment statistics while under-reporting when unemployment statistics are positive (Garz, 2013).
Gotcha Journalism: Gotcha journalism involves having journalists go out and actively seek out “gotcha questions” that will lead to sensational headlines. It is a form of bias because it often leads to less reporting on substantive messaging and an over-emphasis on gaffes and disingenuous characterizations of politicians.
Citizenship bias: When a disaster happens overseas, reporting often presents the number deceased, followed by the number from the news outlet’s company. For example, they might say: “51 dead, including 4 Americans.” This bias, of course, is to try to make the news appear more relevant to their audience, but nonetheless shows a bias toward the audience’s in-group.
Online indie media bias: Online indie media groups that have shot up on YouTube and social media often have overt biases. Left-wing versions include The Young Turks and The David Pakman Show , while right-wing versions include The Daily Wire and Charlie Kirk .
Western alienation: In Canada, this phenomenon refers to ostensibly national media outlets like The Globe and Mail having a bias toward news occurring in Toronto and ignoring western provinces, leading to “western alienation”.

The Government’s Role in Media Bias

Governments also play an important role in media bias due to their ability to distribute power.

The most obvious examples of pro-government media bias can be seen in totalitarian regimes, such as modern-day North Korea (Merloe, 2015). The government and the media can influence each other: the media can influence politicians and vice versa (Entman, 2007).

Nevertheless, even liberal democratic governments can affect media bias by, for example, leaking stories to their favored outlets and selectively calling upon their preferred outlets during news conferences.

In addition to the government, the market can also influence media coverage. Bias can be the function of who owns the media outlet in question, who are the media staff, what is the intended audience, what gets the most clicks or sells the most newspapers, and so on.

Media bias refers to the bias of journalists and news outlets in reporting events, views, stories, and everything else they might cover.

The term usually denotes a widespread bias rather than something specific to one journalist or article.

There are many types of media bias. It is useful to understand the different types of biases, but also recognize that while good reporting can and does exist, it’s almost impossible to fully eliminate biases in reporting.

Aritenang, A. (2022). Understanding international agenda using media analytics: The case of disaster news coverage in Indonesia. Cogent Arts & Humanities , 9 (1), 2108200.

Brandenburg, H. (2006). Party Strategy and Media Bias: A Quantitative Analysis of the 2005 UK Election Campaign. Journal of Elections, Public Opinion and Parties , 16 (2), 157–178. https://doi.org/10.1080/13689880600716027

D’Alessio, D., & Allen, M. (2000). Media Bias in Presidential Elections: A Meta-Analysis. Journal of Communication , 50 (4), 133–156. https://doi.org/10.1111/j.1460-2466.2000.tb02866.x

Eberl, J.-M., Boomgaarden, H. G., & Wagner, M. (2017). One Bias Fits All? Three Types of Media Bias and Their Effects on Party Preferences. Communication Research , 44 (8), 1125–1148. https://doi.org/10.1177/0093650215614364

Eberl, J.-M., Wagner, M., & Boomgaarden, H. G. (2018). Party Advertising in Newspapers. Journalism Studies , 19 (6), 782–802. https://doi.org/10.1080/1461670X.2016.1234356

Entman, R. M. (2007). Framing Bias: Media in the Distribution of Power. Journal of Communication , 57 (1), 163–173. https://doi.org/10.1111/j.1460-2466.2006.00336.x

Garz, M. (2014). Good news and bad news: evidence of media bias in unemployment reports. Public Choice , 161 (3), 499-515.

Groeling, T. (2013). Media Bias by the Numbers: Challenges and Opportunities in the Empirical Study of Partisan News. Annual Review of Political Science , 16 (1), 129–151. https://doi.org/10.1146/annurev-polisci-040811-115123

Groseclose, T., & Milyo, J. (2005). A measure of media bias. The Quarterly Journal of Economics , 120 (4), 1191-1237.

Groseclose, T., & Milyo, J. (2005). A Measure of Media Bias. The Quarterly Journal of Economics , 120 (4), 1191–1237. https://doi.org/10.1162/003355305775097542

Haselmayer, M., Meyer, T. M., & Wagner, M. (2019). Fighting for attention: Media coverage of negative campaign messages. Party Politics , 25 (3), 412–423. https://doi.org/10.1177/1354068817724174

Haselmayer, M., Wagner, M., & Meyer, T. M. (2017). Partisan Bias in Message Selection: Media Gatekeeping of Party Press Releases. Political Communication , 34 (3), 367–384. https://doi.org/10.1080/10584609.2016.1265619

Hofstetter, C. R., & Buss, T. F. (1978). Bias in television news coverage of political events: A methodological analysis. Journal of Broadcasting , 22 (4), 517–530. https://doi.org/10.1080/08838157809363907

Mackey, T. P., & Jacobson, T. E. (2019). Metaliterate Learning for the Post-Truth World . American Library Association.

Merloe, P. (2015). Authoritarianism Goes Global: Election Monitoring Vs. Disinformation. Journal of Democracy , 26 (3), 79–93. https://doi.org/10.1353/jod.2015.0053

Mullainathan, S., & Shleifer, A. (2002). Media Bias (No. w9295; p. w9295). National Bureau of Economic Research. https://doi.org/10.3386/w9295

Newton, K. (1996). The mass media and modern government . Wissenschaftszentrum Berlin für Sozialforschung.

Raymond, C., & Taylor, S. (2021). “Tell all the truth, but tell it slant”: Documenting media bias. Journal of Economic Behavior & Organization , 184 , 670–691. https://doi.org/10.1016/j.jebo.2020.09.021

Ribeiro, F. N., Henrique, L., Benevenuto, F., Chakraborty, A., Kulshrestha, J., Babaei, M., & Gummadi, K. P. (2018, June). Media bias monitor: Quantifying biases of social media news outlets at large-scale. In Twelfth international AAAI conference on web and social media .

Sloan, W. D., & Mackay, J. B. (2007). Media Bias: Finding It, Fixing It . McFarland.

van Dalen, A. (2012). Structural Bias in Cross-National Perspective: How Political Systems and Journalism Cultures Influence Government Dominance in the News. The International Journal of Press/Politics , 17 (1), 32–55. https://doi.org/10.1177/1940161211411087

Chris Drew (PhD)

Dr. Chris Drew is the founder of the Helpful Professor. He holds a PhD in education and has published over 20 articles in scholarly journals. He is the former editor of the Journal of Learning Development in Higher Education. [Image Descriptor: Photo of Chris]

Chris Drew (PhD) https://helpfulprofessor.com/author/chris-drew-phd/ 10 Elaborative Rehearsal Examples
Chris Drew (PhD) https://helpfulprofessor.com/author/chris-drew-phd/ Maintenance Rehearsal - Definition & Examples
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BIASED MEDIA: A THREAT TO INDIAN DEMOCRACY

Subject-wise Law Notes
March 30, 2020

In the mass media era, the role of the media was universally regarded as fundamental to the proper functioning of the democratic state: the media’s capacity to provide information freely to all citizens ensured they had equal access to the democratic process. Media is being considered as the fourth pillar of democracy . It is easy to demonstrate how the flow of information could be manipulated and the power of the media abused. The modern era seems to be the of a media . In this world of technology we often come through fake news, hate speech, revenge porn and so on. In this article, I want to discuss some aspects of what we have got from the digital mediums so far, with a particular focus on the changing relationship between the media and democracy – and within that, the role of news, information and the practice of journalism. Media is being considered as “Silent revolution” in 21st century.

I INTRODUCTION

“Just because something is not a lie does not mean that it is not deceptive . A liar knows that he is a liar, but one who speaks mere portions of truth in order to deceive is a craftsman of destruction.”

– Criss Jami

Media has played a significant role in establishing democracy throughout the world including India. Since 18th century the media has been instrumental in generating awareness and in spreading knowledge across the masses especially in the American Independence movement and the French Revolution . Media did the same work for India also . Media has played a very critical role even in colonial India. Media is like an eye of an eagle which keeps an eye on the activities of a state.

In our Independence media did the same work. The fallacies of British ruled government being good for the growth and development of colonial India were shattered by means of the media. Media has proved to be the sole source of information for our citizens (i.e. our Indian brothers) of colonial India to become aware of the arbitrariness and brutality of the British Colonial rule. Media played very significant role in communicating the non- humanitarian acts to the people of India. Media is considered as the fourth pillar of democracy along with Executive , legislation and Judiciary and rightly so.[1]Media was not only used after independence but before independence also. From pre- independence India where Mahatma Gandhi used journals “ Young India” and “Harijan” to communicate is message to the people of his country and also to organize National Movement in the country at the wider level. But today we use different/ newer method/media like social media.

II WHAT DOES DEMOCRACY MEAN ?

The world democracy has been conveniently interpreted as the right to vote. In a study , Ober has analysed the word “Democracy”. The origin of the word democracy is Greek word ,a combination of Demos (the people) and kratos (power), thus meaning “the power of the people” which, in turn , means “majority rule” by the people or their elected representatives. It is generally construed as the power to vote and elect a government.

However, Ober ( 2008) discuss how the word democracy actually means “capacity to do things” and not just “majority rule” . It is the power to be able to participate in the democratic process of a country , and not just power to vote a popular government to power. In democracy, every citizen has a right to freedom of expression. This also means that one can express in without the fear of any repercussion and without the fear of authority, and that the citizen and the authority are on an equal platform during the changes.[2] In short, in a democracy all citizens have equal right to voice their concerns, and to speak their mind.

In the recent elections 2019 Lok Sabha election it is apt to say that although role of media has undergone massive changes it still remains a critical pillar in the Indian democratic system . Media has played its very important role very well. However, it is necessary for the progression of media it is necessary that media of that should be free from any constraint and ill-pressure. However, the Independence of this crucial fourth pillar has often been threatened which has led to the doubts about its reliability and authenticity. Press censorship during the 1975 National Emergency to the more recent shutdown of a news channel during reporting of the 2016 Pathankot attack and Pulwama attack show restricted press freedom , with NGO reporters without borders ranking .

India has been ranked 140 and its abuses score is 65.25 as compared to the 2018 which has decreased to -2 in World press freedom index . Index indicating the same the World Press Freedom Index takes into account factors like pluralism , self-censorship, media independence, transparency, violence against journalists and media persons. India is being criticized in world for its sensationalism and manipulation of the fact by selective portrayal of the audiences like mostly news is presented to the masses by concealing facts.

Social media has added to the Fire of Sensationalism. With the urgent need to know the information and dispense it amongst the masses first has often led to mis-reporting of fake news resulting in controversies and defamation in some cases. For example GPS chip in 500 and 2000 Rs. notes after demonetisation.

In common parlance social media is seen synonymous with Facebook , Twitter, WhatsApp, LinkedIn etc. However, social media goes beyond this and has a broader scope. It is an umbrella term that refers to various websites in application that enables users to create share content, interact and to participate in social networking and to put out their views on the social networking. Social media has been often described as a “Silent Revolution” of the “21st century”. Movements initiated from these platforms like – #MetooMovement , #BlackLivematters and various schemes of the Government of India also have been launched from these platforms and are communicated to the masses.

United Nations has made internet access a human right. Recently Supreme Court has declared right to Internet access as a Fundamental right of every Indian . It is said that social media is a light arm of anarchy, because it can be used as a measure to disturb the tranquility of any Nation and to a very extent this is true it affects the peace of a nation . Social media is being used by terror organisations like ISIS to fulfil their agenda and disturb the political and economic stability of the country as this affects very badly. Social media is being used by Isis to promote religious fundamentalism and favouritism in the country.[3]

Recently last year a scene of Bhojpuri was circulated in West Bengal which led to communal rights in society and polarisation of society . Social media is also used for spreading fake news deliberately so that a particular segment of people can benefit from that joke, rumours . For example few years back a news spread that there was lynching of north eastern people in Bangalore which resulted in mass Exodus from the Bangalore of north eastern people . Another rumour spread that there were some people who slaughtered cow and the public in fit of Rage of anger they lynched the people. Another incident that took place in up that there were some people abducting children. This rumour spreaded so fast that people without any reason targeted some people and burst their anger on another people .

Every political party use media selflessly to promote their policies agenda and illiciting support for their campaign. Social media also become a platform to glorify political leaders prior to elections. With no proper regulatory Framework in place for social media, this avenue is often violated to spread fake news , a tool for propaganda as well as foreign interference in domestic election. Social media sometime prove to be adda of some religious people as they are staunch followers of some particular group because of these views violence in society is there .[4]

Last year a guy in Mumbai try to resorted to suicide because he was threatened by people of religious Organisation own his views on a religious topic. If media can create a war between two parties it can create peace also . Indian government’s external affairs ministry did a tremendous job in providing relief to foreign citizens of the India. Another example is Delhi Metro which uses its Twitter handle to inform the open and closure of gates. Social media became the voice of voiceless people of the society .

Movements like #Metoomovement started which allowed the women to come and file a case against a sexual harassment at workplace. Jon Ranson has said that it has given voice to the voiceless people. Personalities like Nana Patekar ,director Harvey Weinstein and various personalities were there which were caught in this . Social media has played an important role in economic front social media is being used on all interfaces for the promotion in their products . Social media is a medium to raise a voice against government policies and social evils prevailing in a society . Now there has emerged a fifth pillar of a democracy i.e. Social Media.

Social media has become means of free speech and expression guaranteed under the article 19 of Indian constitution this can be manifested from the Supreme Court decision on right to internet as a fundamental right. Social media is proliferating a very good ideas and effecting both positively and negatively there is a need to regulate it . Government should come up with a legislation to regulate social media. Just like a car without the handle is of no use same is with these platforms if they are contolled they can prove to be very dangerous in future. There is need to focus on privacy front as well so that the data cannot be used as a threat to personal and national security. Awareness and awakening about use of social media in need to be increased specially among the youth so that they can maximize on positive front of social media and minimise its ill effect . Indeed social media is a double edged sword if not handled with care might cause harm to you badly . It has transformed the way thinking . it has led to violation of privacy, fake news .

In US where democracy is said to be started has also been in news due to its scandal in election.[5] The Cambridge Analytica scandal where in Facebook data was stolen and has been used to influence the people behaviour towards their respective candidates . Social media’s gruesome face has been seen in the US 2016 elections.[6] Media bias varies in its form sensationalism to story selection and placement as well as omission and selection of content while portraying a story. Often holistic view is deleted and biased extreme lables are promoted to define groups and politicians.[7]

Moreover a recent research project called Media Ownership Monitor carried out in India by reporters without borders and data leads has found excessive political control over media. And the news over which political parties have their influence loses its credibility . News reported within person’s pressure not only loses its credibility but also its authenticity.[8] Like it may not be suitable take a name of a news channel but there are various channels which are owned by the members of the party in power then how can we expect that the news provided by the channels will be given in an impartial manner and will be giving authentic matter. The gradual crippling of democracy is also evident from the concentration of media content in a few hands like 76% of Indians use social media and radio is also owned by the state so it is monopolised. Amartya Sen sees the media as a watchdog not just against corruption but also against disaster. He said “There has never been a famine in a functioning multiparty democracy .A free press and the practice of democracy contribute greatly to bringing out information that can have an enormous impact on policies for famine prevention a free press and an active political opposition constitute the best early-warning system a country threatened by famine could have .[9]

The key issue is the lack of proper regulation of media in India and toothless nature of the Press Council of India. The lack of regulation has increased cross holding and corporate and political lobbying and ownership. The increasing rate for TRP to get advertising revenues had further led to more sensationalized news with minimal forces on development like social welfare government schemes and awareness of the general masses with the rise of social media .

Urgent reforms are need of the hour to revamp the outdated regulation for media in India. When Press Council of India was formed in 1978, media only comprised of newspaper, journals, magazines and TV channels . With increase of internet social media platforms are at its peak so they must also be regulated within the same ambit . The Tamil Nadu government has initiated steps in this regard for social media accountability by proposing to link Aadhaar with social media accounts. Anti-nationalist use media for the radicalisation of the youth and it targets the miserable sections of the society for the polarization of the politics also being promoted by the media while also encouraging hero worship tendencies. The era where media was considered as a guardian of a country and constitution .

Keeping a check on the government and encouraging substantial debate among the masses on critical national issues to participate in democracy is starting fade away. The aforementioned quote by the states that selective truths can lead to weapon of destruction. In the same manner you must look at creating genuinely Independent and transparent system for the media and ensure ownership restricting in cross holding .The Leveson Committee Department of 2012 of UK can be a great static point to adapt into the Indian context ensuring right to free speech in press while curtailing exploitation and misuse of the same . As Victor Hugo said, “No power can on earth can stop an idea whose time has come.” Today this power is social media.

[1] https://opentextbc.ca/mediastudies101/chapter/media-and-democracy/

[2] Dr. K. John Babu, Media and Human rights

[3] Andrew Dewdney and Peter Ride, The new media handbook , London And New York, Taylors and Francis Group

[4] Silvio Waisbord , Media Sociology, Jaipur, Rawat Publications.

[5] https://democracy-reporting.org/country/social-media-and-democracy

[6] https://www.frontiersin.org/research-topics/11319/media-democracy-and-governance

[7] https://legaldesire.com/role-of-media-in-democracy/

[8] https://www.vox.com/policy-and-politics/2019/1/22/18177076/social-media-facebook-far-right-authoritarian-populism

[9] https://legaldesire.com/role-of-media-in-democracy/

Author Details: Rakesh Kumar is a student at Dr. Ram Manohar Lohiya National Law University , Lucknow.

The views of the Author are personal only.

(Source: Juscholars Journal, Volume 1, Issue 3)

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NPR in Turmoil After It Is Accused of Liberal Bias

An essay from an editor at the broadcaster has generated a firestorm of criticism about the network on social media, especially among conservatives.

Uri Berliner, wearing a dark zipped sweater over a white T-shirt, sits in a darkened room, a big plant and a yellow sofa behind him.

By Benjamin Mullin and Katie Robertson

NPR is facing both internal tumult and a fusillade of attacks by prominent conservatives this week after a senior editor publicly claimed the broadcaster had allowed liberal bias to affect its coverage, risking its trust with audiences.

Uri Berliner, a senior business editor who has worked at NPR for 25 years, wrote in an essay published Tuesday by The Free Press, a popular Substack publication, that “people at every level of NPR have comfortably coalesced around the progressive worldview.”

Mr. Berliner, a Peabody Award-winning journalist, castigated NPR for what he said was a litany of journalistic missteps around coverage of several major news events, including the origins of Covid-19 and the war in Gaza. He also said the internal culture at NPR had placed race and identity as “paramount in nearly every aspect of the workplace.”

Mr. Berliner’s essay has ignited a firestorm of criticism of NPR on social media, especially among conservatives who have long accused the network of political bias in its reporting. Former President Donald J. Trump took to his social media platform, Truth Social, to argue that NPR’s government funding should be rescinded, an argument he has made in the past.

NPR has forcefully pushed back on Mr. Berliner’s accusations and the criticism.

“We’re proud to stand behind the exceptional work that our desks and shows do to cover a wide range of challenging stories,” Edith Chapin, the organization’s editor in chief, said in an email to staff on Tuesday. “We believe that inclusion — among our staff, with our sourcing, and in our overall coverage — is critical to telling the nuanced stories of this country and our world.” Some other NPR journalists also criticized the essay publicly, including Eric Deggans, its TV critic, who faulted Mr. Berliner for not giving NPR an opportunity to comment on the piece.

In an interview on Thursday, Mr. Berliner expressed no regrets about publishing the essay, saying he loved NPR and hoped to make it better by airing criticisms that have gone unheeded by leaders for years. He called NPR a “national trust” that people rely on for fair reporting and superb storytelling.

“I decided to go out and publish it in hopes that something would change, and that we get a broader conversation going about how the news is covered,” Mr. Berliner said.

He said he had not been disciplined by managers, though he said he had received a note from his supervisor reminding him that NPR requires employees to clear speaking appearances and media requests with standards and media relations. He said he didn’t run his remarks to The New York Times by network spokespeople.

When the hosts of NPR’s biggest shows, including “Morning Edition” and “All Things Considered,” convened on Wednesday afternoon for a long-scheduled meet-and-greet with the network’s new chief executive, Katherine Maher , conversation soon turned to Mr. Berliner’s essay, according to two people with knowledge of the meeting. During the lunch, Ms. Chapin told the hosts that she didn’t want Mr. Berliner to become a “martyr,” the people said.

Mr. Berliner’s essay also sent critical Slack messages whizzing through some of the same employee affinity groups focused on racial and sexual identity that he cited in his essay. In one group, several staff members disputed Mr. Berliner’s points about a lack of ideological diversity and said efforts to recruit more people of color would make NPR’s journalism better.

On Wednesday, staff members from “Morning Edition” convened to discuss the fallout from Mr. Berliner’s essay. During the meeting, an NPR producer took issue with Mr. Berliner’s argument for why NPR’s listenership has fallen off, describing a variety of factors that have contributed to the change.

Mr. Berliner’s remarks prompted vehement pushback from several news executives. Tony Cavin, NPR’s managing editor of standards and practices, said in an interview that he rejected all of Mr. Berliner’s claims of unfairness, adding that his remarks would probably make it harder for NPR journalists to do their jobs.

“The next time one of our people calls up a Republican congressman or something and tries to get an answer from them, they may well say, ‘Oh, I read these stories, you guys aren’t fair, so I’m not going to talk to you,’” Mr. Cavin said.

Some journalists have defended Mr. Berliner’s essay. Jeffrey A. Dvorkin, NPR’s former ombudsman, said Mr. Berliner was “not wrong” on social media. Chuck Holmes, a former managing editor at NPR, called Mr. Berliner’s essay “brave” on Facebook.

Mr. Berliner’s criticism was the latest salvo within NPR, which is no stranger to internal division. In October, Mr. Berliner took part in a lengthy debate over whether NPR should defer to language proposed by the Arab and Middle Eastern Journalists Association while covering the conflict in Gaza.

“We don’t need to rely on an advocacy group’s guidance,” Mr. Berliner wrote, according to a copy of the email exchange viewed by The Times. “Our job is to seek out the facts and report them.” The debate didn’t change NPR’s language guidance, which is made by editors who weren’t part of the discussion. And in a statement on Thursday, the Arab and Middle Eastern Journalists Association said it is a professional association for journalists, not a political advocacy group.

Mr. Berliner’s public criticism has highlighted broader concerns within NPR about the public broadcaster’s mission amid continued financial struggles. Last year, NPR cut 10 percent of its staff and canceled four podcasts, including the popular “Invisibilia,” as it tried to make up for a $30 million budget shortfall. Listeners have drifted away from traditional radio to podcasts, and the advertising market has been unsteady.

In his essay, Mr. Berliner laid some of the blame at the feet of NPR’s former chief executive, John Lansing, who said he was retiring at the end of last year after four years in the role. He was replaced by Ms. Maher, who started on March 25.

During a meeting with employees in her first week, Ms. Maher was asked what she thought about decisions to give a platform to political figures like Ronna McDaniel, the former Republican Party chair whose position as a political analyst at NBC News became untenable after an on-air revolt from hosts who criticized her efforts to undermine the 2020 election.

“I think that this conversation has been one that does not have an easy answer,” Ms. Maher responded.

Benjamin Mullin reports on the major companies behind news and entertainment. Contact Ben securely on Signal at +1 530-961-3223 or email at [email protected] . More about Benjamin Mullin

Katie Robertson covers the media industry for The Times. Email: [email protected] More about Katie Robertson

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Insights Weekly Essay Challenges 2021 – Week 55 : Biased Media Is A Real Threat To Indian Democracy

Insights weekly essay challenges 2021 – week 55.

02 January 2021

Write an essay on the following topic in not more than 1000-1200 words:

Biased Media Is A Real Threat To Indian Democracy

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Biased Media is a real threat to Indian Democracy..

Media are the communication outlets or tools used to store and deliver information or data. The term refers to components of the mass media communications industry, such as print media, publishing, the news media, photography, cinema, broad casting (radio and television) and advertising.

Biased journalist or biased news channel shows that all policies and steps of government or apolitical party is always right, they do not criticize government for their wrong work and this will harm the democracy or country because criticism is the backbone of democracy, criticism keeps the government on right track, and media is the fourth pillar of democracy, media keeps democracy alive.

Security implications from Social Media:

As technology is a double edged sword. The large numbers, speed, anonymity and secrecy attached to these conversations have far reaching security implications. Subversive actors have proved in recent years that they are particularly adept at utilizing the Internet and social media to facilitate their activities.

The security implications include:

Radicalization: Terrorist groups like Islamic State (ISIS) and Al Qaeda and countries like Pakistan have been extremely effective in using social media to radicalize people and position them to commit violent acts.
Terrorism: Many terror modules were busted by police in India, all of whose members were groomed, trained, funded and armed by their handlers on social networking sites. World over, there are cases of terrorist operations, especially lone wolf attacks, being coordinated through social media.
Incitement of riots through hateful posts and communal videos. E.g. Hate videos were circulated before the Muzaffarnagar riots of 2013. Pakistan's ISI is known to incite violence by circulating fake videos on social media to incite riots.
Cyber-crime: These include cyber bullying or stalking, financial frauds, identity theft etc.
Divulgence of sensitive information: Forces posted in sensitive locations are prone to giving away their locations and assets on social media.
Influencing democratic processes: The latest emerging threat to national interests is the use of these sites to influence and subvert democratic processes by actors both from within and from enemy countries. Examples recently were seen in US Presidential elections and Brexit referendum.
Cyber espionage: Sensitive information from the mobile phones used by security personnel can be stolen using malware and social media.

Following Measures should be taken to deal with these threats:

Legal Provisions: IT Act 2000 under Sections 69 and 69A provides government with the power to intercept and block any information, as well as punish perpetrators, in the interest of security and public order etc. The Unlawful Activities Prevention Act (UAPA) and IPC also have provisions against spreading hatred between groups, inciting violence and the intent or act of terrorist activities.
Security agencies: Government agencies including National Cyber Coordination Centre (NCCC) and Intelligence agencies actively track terrorist activity on the social media. State police also have their own social media cells, like the highly effective Mumbai's Social Media Lab.
Centralized Monitoring System (CMS): To automate the process of lawful interception and monitoring of the internet in the country. It has come into operation in Mumbai and will soon spread to other areas.
De-radicalisation: The Union Home Ministry initiated counter-radicalisation and de-radicalisation strategy in sync with cultural, education and employment activities to counter the threat.
Guidelines for armed forces: The Government of India issued updated guidelines in 2016 for regulating sharing of secret operational and service data on social media platforms.
Monitoring social networking companies: The activities and influence of social networking sites is also being monitored by the government so that they prevent misuse of their platforms for subversive activities and other cyber threats.
International Cooperation is being promoted to deal with the often transnational nature of the threats.

In view of the broad threat posed by social media, the Union government needs to come up with a National Social Media Policy. All possible legal, administrative and security related efforts must be taken up to check the use of social media for subversive purposes. However, the need for privacy and security has to be balanced carefully.

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Inside CBC, concerns emerge about broadcaster’s response to Palestine essay

Several employees at cbc say the anti-palestinian bias documented in the breach reflect their own experiences.

At CBC’s Toronto headquarters last Thursday, an employee walking through the newsroom noticed several people’s monitors displaying the same page: an essay in The Breach , written by one of their former colleagues, revealing behind-the-scenes details about the broadcaster’s reporting on Israel-Palestine.

Meanwhile, CBC’s top managers were scrambling to meetings to discuss how to handle any fallout from the exposé, which was widely shared on social media, prompting calls for the reevaluation of its approach.

The essay—in which a producer of Jewish background shared her experiences witnessing biased decision making and unprecedented scrutiny of Palestinian guests, before being labelled antisemitic for raising concerns about the coverage—spread like wildfire among staff at the public broadcaster, according to conversations The Breach conducted with several CBC employees.

It elicited a quick public response , less than 24 hours after publication, from CBC editor in chief Brodie Fenlon, who wrote that the essay’s “broad conclusions are not true,” without addressing the specific assertions in the article.

But in its latest response to The Breach, CBC has shifted its tune, suggesting that its coverage over the past months has “improved” because it took seriously the “feedback” of the author of the Breach essay.

In interviews, five employees who work in radio and broadcasting said the article resonated with them, shared similar experiences of navigating anti-Palestinian bias, and expressed fears of being falsely labelled as antisemitic. One veteran producer said the editor in chief’s denial’s were “ludicrous.”

All said the essay elicited mixed reactions among their colleagues, with more precarious, younger workers tending to identify more with author Molly Schumann, and more established employees tending to defend CBC’s top managers.

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All five, who requested to remain anonymous for fear of retaliation, said they were either baffled or insulted by Fenlon’s note—first sent in an email to staff and later published online—in response to the essay. For the sake of clarity, The Breach has added gender-neutral pseudonyms to some sources in this article to protect their anonymity.

Biases in CBC coverage of Israel-Palestine have been documented in previous years by various sources, including the Toronto Metropolitan University’s Review of Journalism and senior CBC employee Pacinthe Mattar in the Walrus .

Reports in The Breach since Oct. 7 have revealed its flagship shows aired far fewer Palestinian voices than Israelis, and that senior managers of journalistic practices at CBC believe it is justified to use less emotive language to describe the killing of Palestinians compared to Israelis.

This existing context was raised by several of the employees interviewed by The Breach.

The Breach sent multiple specific questions to CBC about employees’ responses to the Breach essay, which it answered with a statement.

“We have taken a number of measures to support staff through [the story], always working toward a more inclusive newsroom environment. Those measures include training, coaching for editorial leaders, and our peer-to-peer support network,” wrote Chuck Thompson, head of public affairs at CBC.

“Our coverage is strong and has improved, in part thanks to internal feedback by many employees from a wide range of backgrounds and seniority levels, including months ago from the author of The Breach essay. Critical voices are not silenced, they are encouraged.”

Palestine, a workplace security issue

Cam, a radio producer who has worked at CBC for decades, said they were most troubled by the accusation of antisemitism levied by co-workers against the author of The Breach essay, the pseudonymous Molly Schumann.

In a team meeting Schumann described in The Breach, she said that a factor shaping their coverage was the fear of getting flooded by email complaints from pro-Israel lobby groups like Honest Reporting Canada.

A week later, a manager informed Schumann that someone had accused her of antisemitism for referring to the “Jewish lobby.”

But recordings of the meeting reviewed by The Breach prove that Schumann—whose father is a Holocaust survivor—did not say those words.

In response to The Breach’s questions about the antisemitism allegation, CBC spokesperson Chuck Thompson said the corporation is “limited in what we can say” about “confidential employee matters.”

“A number of employees expressed concerns that some of her comments and behaviour in a team meeting were hurtful, disrespectful and discriminatory,” he added.

The false allegation of antisemitism levied against Schumann is, for Cam and some of their colleagues, a workplace safety issue.

“If she hadn’t taped that conversation, the accusation of what she was alleged to have said by whatever colleague may have stood,” Cam said. “Are we supposed to walk around thinking that false allegations are going to be made about us in terms of what we say about the story, and we should all be recording our conversations?”

The fact that Fenlon and management’s response did not address this, “I found really negligent, as did other people,” Cam said.

Job security is a major factor, a number of employees said, in their ability to speak up. Over a quarter of CBC’s workforce are temporary.

When a producer on Cam’s team objected to a Palestinian historian being put on air, Cam could ignore them. “I have the comfort of being able to do that because I have 30 years of experience and I’ll retire in three years,” they said. “But I know the younger ones don’t.”

In another newsroom, for a different CBC show, a younger producer said they have been having a difficult time getting Palestinian voices on air for the same reasons Schumann highlighted in her essay.

“I’m just concerned that if I keep raising these issues, I’m going to get labelled as an antisemite,” they said.

Another temporary employee, a reporter on a multimedia unit, said that despite some shifts, it’s a problem that the word “Palestine” will still get edited out of drafts.

“Even just being comfortable enough to have these sorts of conversations and to be more critical,” they said. “It depends on how stable you are at your job and I think all of that factors into whose opinions get heard.”

Acts of editorial resistance

In their team, Cam says they regularly pitch coverage of Gaza and the war’s connections to Canada, which gives younger members of their team space to do the same.

But across the nearly 50 cities in Canada that the CBC operates in, many teams don’t have senior members like Cam.

Del, a pseudonym for a producer on the opposite side of the country from Cam, said some of the conversations Schumann wrote about in the essay “played out almost word for word in my station too.”

“It was kind of validating to be like I’m not alone,” they said. “Our newsroom isn’t the only one that’s facing these issues. It goes all the way to the top.”

Some older colleagues on Del’s team and other teams celebrated Fenlon’s response and criticized the author of The Breach essay for writing under a pseudonym. In some units, colleagues spent time trying to figure out the real identity of Molly Schumann.

“I find this happens any time the CBC gets scrutinized in a public way,” Del said. “Some of the younger staff will be like ‘100 per cent this criticism is valid, we can see this happening.’ And among the old guard, their instinct will automatically be to get defensive and dismissive, instead of maybe contemplating or digging into why this criticism is arising.”

At Del’s station, they said reporters’ scripts “would get edited to the point of meaninglessness” when they tried to interview anti-war protesters who had been gathering in their city weekly since mid-October. Guests, including an acclaimed former CBC reporter, were told their interview would be pre-taped instead of being live because of what language they may use to describe the Israeli assault on Gaza.

Still, Del and another producer on their team said reporters pushed back on their local managers’ attempts to muzzle Palestinians or guests speaking in support of Palestinian rights.

“There were times when we changed scripts back to their original, accurate wording after they were sanitized or de-fanged by higher-ups,” Del said, adding that some producers who “got it” would keep guests’ words intact and used active language in their scripts like “an Israeli bombing killed Palestinians” instead of “Palestinians were reported dead.”

Change at a glacial pace

None of the people interviewed said they are hopeful that the essay will cause CBC managers to properly address clear anti-Palestinian biases in coverage.

For Cam, 30 years at the public broadcaster has taught them that it’s “ludicrous” to say there is no anti-Palestinian bias at CBC, as Fenlon’s note suggested. They believe that lasting change at the corporation happens at a glacial pace.

“That reluctance to put on Palestinian voices has always been there,” Cam said. “But honestly at one point, there were none. At least now, there is a comfort level in some quarters that it’s okay to talk to Palestinians, or Middle Eastern people for that matter, about this story. Much more than there was five years ago, where they would have always been seen to have a bias and not to be trusted sources.”

On the other hand, several employees noted that since Oct. 7, Israeli officials and pro-Israel commentators were often allowed to go unchecked on live radio and broadcast segments.

“I have seen egregious things happen,” Cam said, “like them putting on an Israeli official in the middle of December and letting him go off about beheaded babies and how Hamas is coming for us in Toronto and Vancouver and not challenging him.”

After months of navigating these practices, slow changes at CBC are too little, too late for some employees.

Alex, a pseudonym for a radio and broadcast producer, left CBC a few weeks ago after three years of working at the public broadcaster. He told The Breach it was because of the “moral baggage of being associated with a organization that I do believe has and continues to successfully manufacture consent for a genocide.”

According to Alex, their manager repeatedly removed their guests’ claims of genocide being committed by Israel in Gaza, from their tapes. The more Alex pitched segments about growing Palestinian solidarity protests in their city, the more uncomfortable they said their supervisor would get, at times even removing him from covering the story he’d pitched.

“The foot soldiers, the people who are making the operation work, are on the same page, but then there’s this disconnect with management.”

Palestine | CBC has whitewashed Israel’s crimes in Gaza. I saw it firsthand

Palestine | CBC says killing of Palestinians doesn’t merit terms ‘murderous,’ ‘brutal’

Corporate Media | CTV forbids use of ‘Palestine,’ suppresses critical stories about Israel

Like the NYT, CBC reflects the government’s foreign policy. If they hadn’t, Human Rights Watch documentation of the monthly killings of Palestinian children by the IDF would have been exposed by now.

This is not an isolated case of lethal bias in CBC’s journalism.

For decades, CBC has disappeared the horrific abuse, in health care and other venues, of Canadians whose psycho-social problems result from the psychiatric sequelae of environmental sensitivities, despite ample evidence.

My career as a CBC journalist ended when I blew the whistle on abusive journalism based on misconceptions and stereotypes.

Brodie Fenlon was, again in this instance, a source of illogical, abusive bigotry.

Thank you for continuing to work at honest reporting of the plausible genocide being perpetrated by Israel on Gaza inhabitants (ICJ, ICC) and, for calling out of our ‘national broadcaster’, CBC. I can’t imagine the courage it takes to maintain your integrity!

Just sayin’, CBC headquarters are in Ottawa, not Toronto.

Thank you, The Breach, for staying with this critical issue at Canada’s public broadcaster. What is happening at CTV bothers me, but bias at CBC bothers me a lot more. It is supported by the public and has a greater obligation to ensure fair presentation of critical issues. The situation has been made worse by the reduction in CBC’s global presence, depending instead on wire services and material of dubious provenance. I stopped watching and listening to its world coverage a long time ago.

Breach is the best news site I have seen in over 60 years of reading news. Congratulations. I sincerely hope you will continue your unbiased reporting. Kudos to Molly Schumann for starting a revolution in journalism!

I believe that this very interesting article forces us to rethink how media operate. Journalism is, first and foremost, the work of people who convey facts in a neutral and objective way, thus allowing the reader / viewer / listener to make up his/her own mind on the meaning of the event(s) depicted. I believe that the arrival of continous, 24 / 7 cable news networks has warped the perception of the work behind journalism. With people constantly on the air, journalists have become analysts of the events on which they report, trying to give sense and meaning, doing the “work” of critical thought that should be left to the news consumer.

In today’s media environnment, there is more analysis and commenting than there is reporting of the facts on the ground. Even in newspapers, the pages are filled with chronicles rather than articles. In media, opinion should be the domain of editorial, not that what appears on the front page.

At the end of the day, I want the facts. I can analyse them and give them the meaning I believe they have. I don’t need someone doing that for me.

Love and appreciate your coverage. Would like to support but lost my job due to covid bs. I know it’s old news but how about something on the Covid Brainwashing Corporation’s complicity.

essay on biased media

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