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Social media addiction: Its impact, mediation, and intervention

Vol.13, no.1 (2019).

Yubo Hou Dan Xiong Tonglin Jiang Lily Song Qi Wang

https://doi.org/10.5817/CP2019-1-4

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This research examined the relations of social media addiction to college students' mental health and academic performance, investigated the role of self-esteem as a mediator for the relations, and further tested the effectiveness of an intervention in reducing social media addiction and its potential adverse outcomes. In Study 1, we used a survey method with a sample of college students ( N = 232) and found that social media addiction was negatively associated with the students' mental health and academic performance and that the relation between social media addiction and mental health was mediated by self-esteem. In Study 2, we developed and tested a two-stage self-help intervention program. We recruited a sample of college students ( N = 38) who met criteria for social media addiction to receive the intervention. Results showed that the intervention was effective in reducing the students’ social media addiction and improving their mental health and academic efficiency. The current studies yielded original findings that contribute to the empirical database on social media addiction and that have important theoretical and practical implications.

Peking University, China

Yubo Hou is an associate professor at Peking University's School of Psychological and Cognitive Sciences and Beijing Key Laboratory of Behavior and Mental Health, as well as a core member of the Center for Cultural Psychology at Tsinghua University. His major research interests include organizational behavior, personality and social psychology, social media, and cultural psychology. He is well-known for his cross-cultural research on the thinking styles of Chinese and Western populations. His current work focuses on behavioral problems and Confucian style of coping among Chinese adults, and the influence of social media on psychological wellbeing. Hou holds a BSc in Psychology from Zhejiang University and a Ph.D. in Social Psychology from Peking University.

Southwest University, China, Peking University, China

Dan Xiong, Assistant Professor, Faculty of Psychology, Southwest University

Tonglin Jiang

Peking university, china the university of hong kong, hong kong.

Tonglin Jiang, Ph.D candidate, Department of Psychology, The University of Hong Kong.

Lily Song, Ph.D candidate, Institute of Psychology, Chinese Academy of Science.

Cornell University, The USA

Qi Wang is a professor and department chair in Human Development at Cornell University. Her research integrates developmental, cognitive, and sociocultural perspectives to examine the mechanisms underlying the development of a variety of social-cognitive skills, including autobiographical memory, self, future thinking, and emotion knowledge. She has undertaken extensive studies to examine how cultural beliefs and goals influence social cognitive representations and processes by affecting information processing at the level of the individual and by shaping social practices between individuals. In addition, she has conducted studies to examine the impact of Internet technology as a cultural force unique to our time on cognitive functioning and well-being. A graduate of Peking University, China, Qi Wang earned a Ph.D. in psychology in 2000 at Harvard University. She has received many honors and awards and has over one hundred and fifty publications in scientific journals and in volumes of collected works. Her single-authored book, The Autobiographical Self in Time and Culture (2013, Oxford University Press), is regarded as the definitive work on culture and autobiographical memory.

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Introduction

Human beings have fundamental needs to belong and to relate, for which interpersonal communication is the key (Baumeiste<tar, 1995; Wang, 2013). In recent decades, with the development of information technology, especially with the rapid proliferation of Internet-based social media (e.g., Facebook, WeChat, or Instagram), the ways of interpersonal communication have drastically changed (Smith & Anderson, 2018; Stone, & Wang, 2018). The ubiquitous social media platforms and the easy access to the Internet bring about the potential for social media addiction, namely, the irrational and excessive use of social media to the extent that it interferes with other aspects of daily life (Griffiths, 2000, 2012). Social media addiction has been found to be associated with a host of emotional, relational, health, and performance problems (e.g., Echeburua & de Corral, 2010; Kuss & Griffiths, 2011; Marino, Finos, Vieno, Lenzi, & Spada, 2017; Marino, Gini, Vieno, & Spada, 2018). Understanding the causes, consequences, and remedies of social media addiction is thus of paramount importance. In the current research, we examined the relations of social media addiction to college students' mental health and academic performance and the role of self-esteem as a mediator for the relations (Study 1). We further tested the effectiveness of an intervention in reducing social media addiction and its potential adverse outcomes (Study 2).

Social Media Addiction and the Negative Outcomes

Social media addiction can be viewed as one form of Internet addiction, where individuals exhibit a compulsion to use social media to excess (Griffiths, 2000; Starcevic, 2013). Individuals with social media addiction are often overly concerned about social media and are driven by an uncontrollable urge to log on to and use social media (Andreassen & Pallesen, 2014). Studies have shown that the symptoms of social media addiction can be manifested in mood, cognition, physical and emotional reactions, and interpersonal and psychological problems (Balakrishnan & Shamim, 2013; Błachnio, Przepiorka, Senol-Durak, Durak, & Sherstyuk, 2017; Kuss & Griffiths, 2011; Tang, Chen, Yang, Chung, & Lee, 2016; Zaremohzzabieh, Samah, Omar, Bolong, & Kamarudin, 2014). It has been reported that social media addiction affects approximately 12% of users across social networking sites (Alabi, 2012; Wolniczak et al., 2013; Wu, Cheung, Ku, & Hung, 2013).

Many studies on social media usage and mental health have shown that the prolonged use of social media such as Facebook is positively associated with mental health problems such as stress, anxiety, and depression and negatively associated with long-term well-being (Eraslan-Capan, 2015; Hong, Huang, Lin & Chiu, 2014; Malik & Khan, 2015; Marino et al., 2017; Pantic, 2014; Shakya & Christakis, 2017; Toker & Baturay, 2016). For example, the time spent on social media was positively related to depressive symptoms among high school students in Central Serbia (Pantic, Damjanovic, Todorovic, et al., 2012) and among young adults in the United States (Lin et al., 2016). Furthermore, certain categories of social media use have been shown to be associated with reduced academic performance (Al-Menayes, 2014, 2015; Junco, 2012; Kirschner & Karpinski, 2010; Junco, 2012; Karpinski, Kirschner, Ozer, Mellott, & Ochwo, 2013; Al-Menayes, 2014, 2015). For example, Lau (2017) found whereas using social media for academic purposes did not predict academic performance indexed by the cumulative grade point average, using social media for nonacademic purposes (video gaming in particular) and social media multitasking negatively predicted academic performance. A large sample (N = 1893) survey conducted in the United States also found that the time students spent on Facebook was negatively associated with their total GPAs (Junco, 2012). Laboratory experiments have provided further evidence for the negative relation between social media use and academic outcomes. For example, Wood et al. (2012) found that multi-tasking via texting, email, MSN, and Facebook had negative effects on real-time learning performance. Jiang, Hou, and Wang (2016) found that the use of Weibo, the Chinese equivalence of Twitter, had negative effects on information comprehension.

Importantly, frequent social media usage does not necessarily indicate social media addiction (Griffiths, 2010) and therefore does not always have negative implications for individuals’ mental health (e.g., Jelenchick, Eickhoff, & Moreno, 2013) or academic performance (Pasek & Hargittai, 2009). A key distinction between normal over-engagement in social media that may be occasionally experienced by many and social media addiction is that the latter is associated with unfavorable consequences when online social networking becomes uncontrollable and compulsive (Andreassen, 2015). Studies investigating social media addiction have mainly focused on Facebook addiction (e.g., Andreassen et al., 2012; Koc & Gulyagci, 2013; Hong et al., 2014). It has been shown that addiction to Facebook is positively associated with depression, anxiety, and insomnia (Bányai et al., 2017; Koc & Gulyagci, 2013; Shensa et al., 2017; Van Rooij, Ferguson, Van de Mheen, & Schoenmakers, 2017) and negatively associated with subjective well-being, subjective vigor, and life satisfaction (Błachnio, Przepiorka, & Pantic, 2016; Hawi & Samaha, 2017; Uysal, Satici, & Akin, 2013). Research has also suggested the negative impact of social media addiction, and Facebook addiction in particular, on academic performance (Huang, 2014; Nida, 2017).

The Role of Self-Esteem

One factor that may underlie the negative effects of social media addiction is self-esteem. Although viewing or editing one's own online profile enhances self-esteem, according to the Hyperpersonal Model (Amy & Hancock, 2010), social media users are frequently exposed to others’ selective and glorified online self-presentations, which can, in turn, reduce the viewers’ self-esteem (Rosenberg & Egbert, 2011). For example, frequent Facebook users believe that others are happier and more successful than themselves, especially when they do not know well the other users offline (Chou & Edge, 2012). Vogel, Rose, Roberts, & Eckles (2014) suggest that the extent of upward social comparisons on Facebook is greater than the extent of downward social comparisons and that upward social comparisons on social media may diminish self-esteem. Empirical studies have provided support to this proposal. For example, a study by Mehdizadeh (2010) showed that the use of Facebook was correlated with reduced self-esteem, such that individuals who spent a greater amount of time on Facebook per session and who made a greater number of Facebook logins per day had lower self-esteem. Another study found that adolescents’ self-esteem was lowered after receiving negative feedback on social media (Valkenburg, Peter, & Schouten, 2006). Moreover, recent studies have revealed a negative relation between addictive use of social media and self-esteem (e.g., Andreassen et al., 2017; Błachnio, et al., 2016).

A considerable number of studies have shown that low self-esteem is associated with many psychological dysfunctions such as depression and anxiety (e.g., Orth, Robins, & Roberts, 2008; Orth & Robins, 2013; Sowislo & Orth, 2013). Self-esteem has also been shown to be positively associated with academic performance (e.g., Lane, Lane, & Kyprianou, 2004; Lent, Brown, & Larkin, 1986) and further serve as a protective factor against adversities in aiding academic and emotional resilience (Raskauskas, Rubiano, Offen, & Wayland, 2015). It is possible that social media addiction contributes to lower self-esteem, which, in turn, leads to a decrease in mental health and academic performance. In other words, self-esteem may play a mediating role in the relations of social media addiction to mental health and academic performance.

The Present Study

To further examine the relations of social media addictions to individuals’ mental health and academic performance, we conducted two studies. In Study 1, we investigated the relations of social media addictions to mental health and academic performance in college students and examined the role of self-esteem as a potential mediator for the relations. A survey method was used in which participants reported their addiction to social media, as well as their mental health, academic performance, and self-esteem. Built on the findings of Study 1, we designed an experimental intervention in Study 2 to reduce social media addiction and further promote college students' mental health and academic performance.

In both studies, we used the Bergen Social Media Addiction Scale (BSMAS; Andreassen et al., 2017) to measure social media addiction. Based on the general addiction theory, Andreassen and colleagues (2012) first developed the Bergen Facebook Addiction Scale (BFAS), with six items each describing one dimension of addictive behavior (i.e., salience, mood modification, tolerance, withdrawal symptoms, conflict, and relapse). The scale has good psychometric properties, and the addiction can be scored using a polythetic scoring scheme (i.e., scoring 3 or above on at least four of the six items) or a monothetic scoring scheme (i.e., scoring 3 or above on all six items) (Andreassen et al., 2012). One critique of BFAS is that it is specific to Facebook addiction and thus may not be appropriate for examining addiction to online social networking more generally (Griffiths, 2012). Andreassen and colleagues (2017) later revised BFAS into BSMAS, replacing "Facebook" with "social media." It has been shown to have excellent reliability (Cronbach's alpha = .88) for measuring social media addiction. In addition, BSMAS has been used with non-English populations such as Iranian, Italian, and Hong Kong samples and demonstrated robust psychometric properties (Lin, Broström, Nilsen, Griffiths, & Pakpour, 2017; Monacis, De Palo, Griffiths, & Sinatra, 2017; Yam et al., 2018).

Based on the findings of previous studies (e.g., Jiang et al., 2016; Koc & Gulyagci, 2013; Pantic et al., 2012; Rosen et al., 2011; Valkenburg et al., 2006), we hypothesized that social media addiction would be negatively associated with college students’ mental health and academic performance, and that these relations would be mediated by the students’ self-esteem. We further expected that an intervention to reduce social media addiction would alleviate its negative associations with mental health and academic performance.

Study 1 utilized a survey method to investigate the relations of social media addiction to mental health and academic performance in college students and to examine the role of self-esteem as a potential mediator for the relations.

Participants. The participants were undergraduate students recruited through a social psychology course at Peking University, China. A total of 250 students who enrolled in the course participated in the study for one course credit. Among the students, 18 did not complete the questionnaires and were excluded. The final sample thus included 232 participants (117 males, 115 females; Mean age = 19.18 years, SD age = 1.32).

Procedure and Materials. Participants each completed a set of questionnaires in class. They were told that the questionnaires were unrelated to each other and that they should carefully answer all questions.

Social media addiction. The 6-item Bergen Social Media Addiction Scale (BSMAS; Andreassen et al., 2017) was used to measure the participants’ addictive use of social media. The items concern experiences occurring over the past year and are rated on 5-point scales ranging from 1 (Very rarely) to 5 (Very often) (e.g., “ How often during the last year have you felt an urge to use social media more and more?” ). Given the characteristics of social networking sites in mainland China, we replaced the examples of social media sites in the original scale, namely “Facebook, Twitter, Instagram and the like,” with those popular in China, "QQ, Weibo, WeChat and the like” in the instruction. A bilingual researcher translated the scale into Chinese, which was then back translated into English by another researcher. The original English version was compared with the back-translated version to resolve any discrepancies between them. The Cronbach's alpha of the Chinese version in the current sample was 0.81. Participants’ ratings were summed across the 6 items to form a social media addiction score, with higher scores indicating greater social media addiction.

Mental health . Mental health was measured by a 20-item questionnaire adapted by Li and Kam (2002) from the 30-item General Health Questionnaire (GHQ-30; Goldberg, 1972). This questionnaire includes three sub-scales: depression ( Cronbach's α = .65), anxiety ( Cronbach's α = .73), and sense of adequacy ( Cronbach's α = .63). Participants were asked to answer “Yes” or “No” about their feelings in recent weeks (e.g., “I feel that being alive has no meaning,” “I feel unsettled or nervous all day long ,” and “I go happily through daily life” ). The scores for depression and anxiety were reverse-coded. Scores of the three sub-scales were then summed ( Cronbach's α = .80), with higher scores indicating better mental health.

Academic performance. Given that the participants came from diverse majors and different classes, their academic performance was measured by self-reported ranking relative to their respective peers. Participants were asked to rank their academic performance relative to their peers in the past semester as 1) 20% or below; 2) 20 - 40%; 3) 40 - 60%; 4) 60 - 80%; or 5) 80 - 100%.

Self-esteem. The 10-item Chinese version of the Self-esteem Scale ( Cronbach's α = .82; Ji & Yu, 1993) adapted from Rosenberg (1965) was used to measure self-esteem (e.g., “ I feel that I have a number of good qualities ”). Participants answered the questions on 4-point scales ranging from 1 (strongly disagree) to 4 (strongly agree). Higher scores indicated higher levels of self-esteem.

At last, participants were asked to report demographic information including age, gender, only child or non-only child status, and urban or rural residence, and they were fully debriefed and thanked.

Results and Discussion

In the current sample, 41.4% of the participants scored 3 or above on at least four of the six items (the polythetic scoring scheme of BSMAS), and 9.9% scored 3 or above on all six items (the monothetic scoring scheme of BSMAS; Andreassen et al., 2012). Also, 14.7% of the participants could be classified as having social media addiction, whose composite score was above 18 and who scored 3 or above on at least four of the six items. This percentage was close to what was previously reported (12%) in a Chinese sample (Wu et al., 2013). Participants who were only children had poorer academic performance, t (194) = 2.71, p = .007, d = .44, higher levels of self-esteem, t (228) = 2.44, p = .02, d = .38, and lower social media addiction scores, t (228) = -2.58, p = .01, d = -.40, than did those with siblings. Participants who came from cities had higher levels of self-esteem, t (214) = 2.87, p = .005, d = .57, than did those from rural areas. Gender and age were not significantly correlated with any variables.

Following previous studies (Andreassen et al., 2012, 2017; Koc & Gulyagci, 2013; Hong et al., 2014), we treated the social media addiction score as a continuous variable to examine the degree of additive use of social media in relation to mental health and academic performance. Table1 presents the means and standard deviations (SDs) of key variables and the correlations among them. Social media addiction was negatively correlated with mental health, whereby the higher one scored on social media addiction, the poorer mental health he or she had. Social media addiction was also negatively correlated with academic performance as well as self-esteem. Self-esteem, on the other hand, was positively related to mental health. Mental health and academic performance were also positively correlated.

Table 1: Means, SDs and Correlations among Study Variables.


1 Social media addiction	14.77	4.13
2 Self-esteem	29.10	4.19	-.23***
3 Mental health	14.28	3.91	-.29***	.55**
4 Academic performance	3.26	1.15	-.16*	.13	.20*
* < .05, < .01, * < .001

We further conducted partial correlation analyses among the key variables, controlling for demographic variables (i.e., age, gender, only child status, and residence). The pattern of results remained identical. The partial correlations between social media addiction and mental health, academic performance, and self-esteem remained significant, r s(232) = -.29 ( p <.001), -.15 ( p = .048), and -.20 ( p = .007), respectively. Self-esteem and mental health were also significantly correlated, r (232) = .55, p <.001, so were mental health and academic performance, r (232) = .20, p = .007.

Because self-esteem was not correlated with academic performance, the mediation effect was not tested further for academic performance. To test whether self-esteem played a mediating role in the relations of social media addiction to mental health, we conducted three steps of regression analyses (Wen, Hou, & Zhang, 2005). In the first step, we regressed mental health on demographic variables and social media addiction. Social media addiction uniquely predicted mental health, β = - .29, t (210) = -4.28, p < .001. In the second step, we regressed self-esteem on demographic variables and social media addiction. Social media addiction uniquely predicted self-esteem. β = - .19, t (210) = -2.75, p = .007. In the third step, demographic variables were entered in the first layer, social media addiction was entered in the second layer, and self-esteem was entered in the third layer to predict mental health. After self-esteem was entered, the size of the standard regression coefficient of social media addiction decreased from -.29 to -.19, t (209) = -3.26, p = .001, △R 2 = .26, p < .001. Thus, the relation between social media addiction and mental health was at least partially mediated by self-esteem. The mediating effect of self-esteem is shown in Figure 1.

Figure 1. Mediating effect of self-esteem (Study 1). ** p < .01, p *** < .001.

To corroborate the findings, we further tested the mediating effect of self-esteem using a bootstrapping analysis with 5,000 iterations (Preacher & Hayes, 2008). The 95% confidence interval was [-.1807, -.0215], excluding 0, which indicates that the mediating effect of self-esteem was significant. To explore an alternative pathway, we tested the mediating effect of self-esteem with social media addiction as the dependent variable and mental health as the independent variable. The 95% confidence interval was [-.1422, .0844], including 0, indicating that the reverse mediating effect of self-esteem was not significant. Thus, the results support our hypothesis that social media addiction was associated with reduced mental health through lowering individuals’ self-esteem.

Results from Study 1 confirmed our hypotheses that social media addiction was negatively associated with mental health, consistent with findings from previous studies (e.g., Koc & Gulyagci, 2013). Furthermore, as expected, we found that self-esteem played a mediating role in the relation between social media addiction and mental health, and that the reverse mediating effect was not significant. These findings suggest that the negative association between social media addiction and mental health is at least partially accounted for by reduced self-esteem.

In addition, results from Study 1 also confirmed our prediction that social media addiction was negatively related to academic performance, although the relation was not strong. On the other hand, self-esteem was not significantly associated with academic performance, which differed from previous studies (Lane et al., 2004; Lent et al., 1986; Raskauskas et al., 2015). This might be because there was only one self-report item to measure academic performance, which could be vulnerable to the influence of social desirability concerns. In addition, given that we did not measure the time participants spent on social media, it is unclear how social media use may differ from social media addiction in relation to mental health and academic performance. We addressed these limitations in Study 2.

Study 1 showed that the addictive use of social media was common among college students and that it was negatively associated with mental health and academic performance. One important follow-up question is whether social media addiction can be reduced and thus its negative associations with health and academic outcomes be alleviated. No study that we know of has considered intervention options for social media addiction. We therefore designed an intervention program for social media addiction based on Young's (1999) recommendations for the treatment of Internet addiction, and we conducted an experiment to verify its effectiveness.

To design an intervention program for social media addiction, we referred to previous studies on Internet addiction interventions. Research has shown that metacognitive beliefs about one’s thinking and self-regulation influence problematic Internet use and social media addiction (Casale, Rugai, & Fioravanti, 2018; Caselli et al., 2018; Spada, Langston, Nikĉević, & Moneta, 2008). According to the cognitive-behavioral model, cognitive distortions such as the ruminative cognitive style are the primary cause of excessive Internet use (Davis, 2001). These cognitive distortions can be automatically activated whenever there is a stimulus associated with the Internet. A vicious cycle of cognitive distortions and reinforcement then results in negative outcomes. This model has been widely used in addiction research related to pathological Internet overuse (Larose, Lin, & Eastin, 2003; Liu & Peng, 2009; Turel, Serenko, & Giles, 2011). A number of cognitive-behavioral therapy techniques have been recommended for treating Internet addiction (Young, 2007; Gupta, Arora, & Gupta, 2013). Based on this literature, we believe that the cognitive-behavioral approach will be a helpful way to mitigate the negative associations of social media addiction with health and academic outcomes. It will help individuals with social media addiction to recognize their cognitive distortions and further guide them to reconstruct their thinking and behavior.

In Study 2, we combined cognitive reconstruction, reminder cards, and the diary technique (Young, 1999) into a novel intervention program and designed a 2 by 2 mixed-model experiment to test its effectiveness. In line with findings of previous studies on Internet addiction (e.g., Gupta et al., 2013; Turel et al., 2011; Young, 2007), we predicted that compared with a control group, the experimental group who experienced the intervention would show reduced social media addiction and improved outcomes in mental health and academic efficiency. We included measures of multiple outcome variables to achieve more reliable results, including daily social media use time, self-esteem, sleep quality, mental health, emotional state, learning time, and learning engagement.

Participants. Study 2 was conducted at Peking University, China. Participants who exhibited social media addiction were preselected from a pool of 242 undergraduate students who enrolled in a social psychology course (a different pool from Study 1). The students were asked to complete the 6-item BSMAS (Andreassen et al.,2017). Among them, 43 students scored higher than 18 on the composite score and also scored 3 or above on at least four of the six items. These students were selected to participate in Study 2. They were randomly assigned to either an experimental or a control group and were tested both before (Time 1) and after the intervention (Time 2). The study was thus a 2 x 2 mixed-model design. The 21 participants in the experimental group completed all aspects of the intervention and both tests. Five of the 22 participants in the control group dropped out before the completion. Hence, the final sample included 38 participants (18 males, 18 females, two unreported; M age = 19.71, SD age =1.43).

Procedures and Measures. The intervention program was approved by the Research Ethics Committee of the School of Psychological and Cognitive Sciences at Peking University. Prior to the intervention at Time 1, all participants were informed that the purpose of this study was to investigate social media addiction and they were asked to provide informed consent. Participants then completed a survey, which included the measures of social media addiction, self-esteem, and mental health, same as in Study 1. In addition, participants were asked to report their daily social media use time, indicating the number of hours they spent on social media per day. Participants also reported their sleep quality, rating on a 5-point scale ranging from 1 (very bad) to 5 (very good).

Participants in the experimental group then participated in a one-week intervention program, while those in the control group did not receive any instruction during this time. The intervention included two stages. The first stage involved cognitive reconstruction and took approximately 30 minutes (Young, 1999). Participants visited the lab, where they were asked to reflect on their social media use from five respects: How much time they spent on social media per day and per week? What other meaningful things they could do with that time? What were the benefits of not using social media? Why did they use social media and were there alternative way to achieve the purposes? What were the adverse effects of social media use? Participants wrote down their responses. After the reflection, participants were asked to each list on a card five advantages of reducing the use of social media and five disadvantages of excessive use of social media. They were then asked to take a photo of the card and use it as a lock screen of their phones that would serve as a reminder for themselves. They were also instructed to post the card on their desks during the following week.

The second stage of the intervention took place in the following week, during which participants in the experimental group were asked to keep a daily to record their thoughts, emotions, and behaviors related to social media use, as part of the cognitive-behavioral techniques (Young, 1999). Participants reflected on their daily use of social media every night before going to bed, including what social media they used, how long and how they used the social media, their thoughts and emotions related to their social media use, and the strategies they would like to use to reduce social media use. They were also asked to indicate their emotional state and learning engagement, as well as their expected social media use the next day. To ensure that the participants followed the instruction, daily reminders were sent to them to complete the recording. Participants were further instructed to take a photo of their completed recording and send it to a contact researcher of the lab to confirm its completion. The participants’ responses in the daily reflection task were part of the intervention and were not used in analysis.

After the intervention, at Time 2, all participants completed another survey. The measures included social media addiction, daily social media use time, self-esteem, sleep quality, and mental health, same as those at Time 1. In addition, the participants’ learning engagement in the past week was measured by the 17-item Utrecht Work Engagement Scale-Student (UWES-S, Fang, Shi, & Zhang, 2008). Participants answered the questions (e.g., “My study inspires me ”) on 5-point scales ranging from 1 (strongly disagree) to 5 (strongly agree) ( Cronbach's α = 0.93 for the current sample). A total score was summed, with higher scores indicating higher levels of learning engagement. Participants also reported their daily learning time outside the class in the past week and rated on their emotional state in the past week on a scale ranging from 1 (very bad) to 100 (very good).

Finally, participants in the experimental group provided feedback on the effectiveness of the intervention. They answered 7 questions concerning the various aspects of the intervention (e.g., “ Generally, I think the intervention is effective” ) on 5-point scales from 1 (strongly disagree) to 5 (strongly agree) ( Cronbach's α = 0.81). At last, participants were fully debriefed and thanked.

Across all dependent variables, 2 (Group: Experimental vs. Control) x 2 (Test time: Time 1 vs. Time 2) mixed-model analyses were conducted to examine the effect of intervention. First, the analysis on social media addiction score revealed main effects of group, F (1, 36) = 7.89, p = .008, η p 2 = .18, and test time, F (1, 36) = 33.74, p < .001, η p 2 = .48, qualified by a significant interaction, F (1, 36) = 17.92, p < .001, η p 2 = .33. For participants in the experimental group, there was a significant decrease in social media addiction from Time 1 to Time 2 , changing from 20.62 (higher than 18) to 14.62 (lower than 18), t (20) = 7.17, p < .001, d = 1.97. In contrast, for participants in the control group, there was no significant change in their social media addiction, t (16) = 1.13, p = .28, d = .35. Figure 2 illustrates the interaction effect.

Figure 2. Social media addiction as a function of test time and group (Study 2).

The same analysis was conducted to examine the effect of intervention on daily social media use time, self-esteem, sleep quality, and mental health, respectively. Table 2 presents means and standard deviations for all variables and t-tests within each group. For daily social media use time, there was a main effect of test time, F (1, 36) = 26.54, p < .001, η p 2 = .42, qualified by a Group x Test time interaction, F (1, 36) = 10.47, p = .003, η p 2 = .23. Further t-tests within each group showed that whereas the average daily time participants spent on social media was reduced significantly from Time 1 to Time 2 for both groups, the reduction was larger for the experimental group. There was only a main effect of test time for self-esteem, F (1, 36) = 12.67, p = .001, η p 2 = .26, and sleep quality, F (1, 36) = 9.10, p = .005, η p 2 = .20, whereby self-esteem and sleep quality increased from Time 1 to Time 2. However, further t-tests within each group showed that the improvements were only significant for the experimental group, but not the control group. For mental health, a significant Group x Test time interaction emerged, F (1, 36) = 5.69, p = .02, η p 2 = .14. Whereas mental health scores increased from Time 1 to Time 2 for the experimental group, t (20) = 2.55, p = .02, d =.59, there was no change for the control group, t (16) = -.86, p = .40, d =-.19. Taken together, these results suggest that our intervention effectively reduced social media addiction and improved mental health and other outcomes.

Further analyses of the remaining outcome variables at Time 2 showed that compared with the control group, participants in the experimental group exhibited better learning engagement, t (36) = .2.31, p = .03, d =.77, spent more time on their study outside the class, t (36)= 2.28, p = .03, d = .75, and experienced a better emotional state, t (36) = 2.74, p = .01, d =.86, during the intervention period. In addition, participants in the experimental group reported that the intervention was effective: all participants rated over 3 for the overall intervention; 81% rated over 3 for the first stage of the intervention and 90% for the second. All participants reported that the daily reflections were helpful, and 86% of them were willing to continue to participate in similar studies.

Table 2. Mean and standard deviation of Time 1andTime2's test scores of key variables.

Outcome variables	Group(n)	Time 1			Time 2		t	p
Outcome variables	Group(n)	M	SD	M		SD	t	p
Social media addiction	Experimental(21)	20.62	2.16	14.62		3.72	7.17	<.001***
Social media addiction	Control(17)	20.12	2.15	19.18		3.07	1.13	.275
Daily social media use time	Experimental(21)	4.65	2.67	1.56		.98	5.09	<.001***
Daily social media use time	Control(17)	3.85	2.31	3.15		1.48	2.16	.046*
Self-esteem	Experimental(21)	28.67	3.68	30.67		3.17	-3.87	.001**
Self-esteem	Control(17)	27.41	3.18	28.35		3.81	-1.42	.174
Sleep quality	Experimental(21)	3.38	.86	3.95		.86	-3.51	.002**
Sleep quality	Control(17)	3.35	1.00	3.59		.80	-1.07	.299
Mental health	Experimental(21)	13.24	4.45	15.71		3.89	-2.55	.019*
Mental health	Control(17)	13.18	4.07	12.35		4.58	.86	.403

In sum, participants in the experimental group exhibited reduced social media addiction and improved mental health as well as self-esteem and sleep quality after a two-stage intervention, whereas there was no significant change in the control group. The experimental group participants evaluated the intervention to be effective, in line with prior research showing that cognitive reconstruction, the reminder card technique, and daily reflections are effective methods in reducing Internet addiction (Young, 1999). Furthermore, compared with those in the control group, participants who received the intervention spent more time on learning and experienced a higher level of learning engagement and better emotional state. It is noteworthy that although control group participants reported reduced social media use time at Time 2, they did not exhibit reduced social media addiction or significant improvement in any outcome measures. This is consistent with the theoretical notion that the mere social media use time is not equivalent with or sufficient to index social media addiction (Griffiths, 2010; Andreassen, 2015). Together, these findings suggest that our intervention was effective in reducing social media addiction and improving college students’ mental health and learning efficiency.

General discussion

The current studies provided empirical support that social media addiction was negatively associated with college students’ mental health and academic performance (Pantic et al., 2012; Jelenchick et al., 2013). Furthermore, in line with previous findings that social media addiction negatively affects self-esteem (Andreassen et al., 2017; Błachnio, et al., 2016; Chou & Edge, 2012; Vogel et al., 2014) and that low self-esteem is associated with mental disorders (Orth et al., 2008; Orth & Robins, 2013; Sowislo & Orth, 2013), the current research yielded the first empirical finding that self-esteem mediated the relation of social media addiction to mental health. Furthermore, the implementation of an intervention based on the cognitive-behavioral approach (Young, 1999, 2007; Gupta et al., 2013) effectively reduced social media addiction and improved mental health and academic efficiency.

Notably, our results showed that social media addiction was associated with reduced mental health partly through lowering individuals’ self-esteem, and that the reverse mediating effect of self-esteem with mental health as the predictor and social media addiction as the outcome variable was not significant. Nevertheless, it does not rule out the possibility that poor mental health can further contribute to social media addiction. Individuals in poor mental health, including those with low self-worth, may use social media as a compensation for their real-life interpersonal deficiency and further develop excessive dependence on social media (Zywica & Danowski, 2008). Also, individuals in poor mental health often try to use social media to improve their mood and, when this need is not met, their mental condition tends to become worse (Caplan, 2010). Thus, the relation between poor mental health and social media addiction is likely to be bidirectional.

The present studies provided strong support for the relation of social media addiction to academic outcomes by using a variety of measures. Study 1 showed that a self-rank measure of academic performance was negatively associated with social media addiction. This relation was not mediated by self-esteem. Study 2 further showed that an intervention to reduce social media addiction improved learning engagement and increased the time spent on learning outside the class. We speculate that there may be three explanations for the negative relation of social media addiction to academic performance. First, social media addiction may mean more time spent online and less time spent on study. Excessive social media use interrupts students’ time management, which further affects academic performance (Macan et al., 1990). Second, social media addiction may interfere with students’ work by distracting them and making them unable to stay focused. Research has shown that multitasking has negative effects on the performance of specific tasks (Ophir, Nass, & Wagner, 2009). Finally, given that students with social media addiction may be easily distracted, it can be difficult for them to encode and remember what they are learning (Oulasvirta & Saariluoma, 2006).

Our intervention program effectively reduced social media addiction and improved students’ mental health and learning efficiency. This has important practical implications by showing that social media addiction can be mitigated through cognitive reconstruction and the supporting techniques. The stage of cognitive reconstruction helped students realize the negative consequences of their addiction to social media as well as the potential benefits of reducing social media usage. The subsequent application of the reminder card as a lock screen of their phones as well as the daily reflections further reinforced this awareness. These findings suggest that helping college students to gain a better understanding of the adverse effects of social media addiction through cost-efficient self-help interventions can reduce social media addiction and have the potential to improve mental health and academic performance.

The current studies have some limitations. First, participants were recruited through psychology courses at Peking University and the sample sizes were relatively small especially in Study 2, which may limit the generalizability of the findings. Future studies should include more diverse and larger samples to increase external validity. Second, participants in the control group of Study 2 did not receive any instruction during the one-week interval and they could be distracted by things unrelated to the study. Future research should establish more strict control conditions to eliminate any confounding variables. Third, the intervention in Study 2 was limited in length and the post-treatment data were collected only once, right after the intervention ended. It is therefore unclear whether the intervention effects on social media addiction and other outcomes would persist over time. Given that the current intervention program for reducing social media addiction was newly developed, it requires further refinement to improve its effectiveness. In addition, future studies should investigate the bidirectional relation between social media addiction and mental health, using longitudinal approaches to further validate the mediating role of self-esteem and examine other potential mediators such as cognitive distortions for the relations of social media addiction to mental health and other outcomes.

In conclusion, the current research revealed negative associations between social media addiction and college students' mental health and academic performance, and the role of self-esteem as an underlying mechanism for the relation between social media addiction and mental health. A cost-efficient intervention that included cognitive reconstruction, reminder cards, and a week-long diary keeping effectively reduced the addiction to social media and further improved mental health and academic efficiency.

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A review of theories and models applied in studies of social media addiction and implications for future research

Affiliations.

1 School of Information, The University of Texas at Austin, USA. Electronic address: [email protected].
2 School of Information, The University of Texas at Austin, USA. Electronic address: [email protected].
PMID: 33268185
DOI: 10.1016/j.addbeh.2020.106699

With the increasing use of social media, the addictive use of this new technology also grows. Previous studies found that addictive social media use is associated with negative consequences such as reduced productivity, unhealthy social relationships, and reduced life-satisfaction. However, a holistic theoretical understanding of how social media addiction develops is still lacking, which impedes practical research that aims at designing educational and other intervention programs to prevent social media addiction. In this study, we reviewed 25 distinct theories/models that guided the research design of 55 empirical studies of social media addiction to identify theoretical perspectives and constructs that have been examined to explain the development of social media addiction. Limitations of the existing theoretical frameworks were identified, and future research areas are proposed.

Keywords: Facebook addiction; Internet addiction; Literature review; Problematic use; Social media addiction; Theoretical framework.

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Healthcare (Basel)

Investigation of the Effect of Social Media Addiction on Adults with Depression

Serdar aydin.

1 School of Health Sciences, Southern Illinois University Carbondale, 1365 Douglas, Drive, Carbondale, IL 62901, USA; ude.uis@ajas

Orhan Koçak

2 Faculty of Health Science, Istanbul University–Cerrahpasa, 34320 Istanbul, Turkey; [email protected]

Thomas A. Shaw

Betul buber.

3 Department of Social Work, Istanbul University–Cerrahpasa, 34320 Istanbul, Turkey; moc.liamg@69rbbltb (B.B.); [email protected] (E.Z.A.)

Esra Zeynep Akpinar

Mustafa z. younis.

4 College of Health Sciences, Jackson State University, 350 W. Woodrow Wilson Dr, Jackson, MS 39213, USA; [email protected]

Associated Data

The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy and ethical concerns.

This study aimed to investigate the effects of social media addiction on depression in adult individuals. For this purpose, the researchers analyzed whether social media dependence had differing impacts according to various variables (age, gender, the highest level of education, duration of daily use of social media, frequency of social media use, etc.). A sample population of 419 people who live in different provinces in Turkey between 18 and 62 years of age participated in the research. The questionnaire form was developed to obtain the Social Media Dependence Scale (SMDS), Beck Depression Inventory scores, and demographic information from the participants. The research was conducted according to the general screening model. Significant differences were found between depression and social media dependency in variables such as the number of children, age, and income. As a result of the study, when social media addiction was examined in terms of gender among socio-demographic variables, no significant difference was found.

1. Introduction

Today, with easy accessibility to the internet and technological tools, individuals from many age groups can effectively use social media. The use of social media applications has become an essential part of daily life. Healthy internet use helps individuals use multiple skills, such as reading, writing, selecting, and classifying, while collecting information. On the other hand, uncontrolled internet use may adversely affect the individual’s physical, mental, social, and cognitive development [ 1 ]. Behavioral addiction, which is defined as an inability to resist an impulse and an incentive to perform an action that harms the person or others, includes technological addiction types such as internet, smartphone, game, and social media addiction [ 2 ]. In 2018, the Internet and Social Media User Stats reported that globally, 42% of the total population used social media, and 51 million people use social media in Turkey, representing 63% of its total population [ 3 ]. Additionally, 2.95 billion people globally, and 44 million people (54%) in Turkey, have access to social media only through smartphones. According to the same report, 9 million people are on the internet a day. averaging 7 h, and 48 million people spend about 2 h on social media per day in Turkey. Social media tools such as Facebook and Myspace, which can connect people and allow for sharing thoughts, are preferred by millions of users, and those platforms attract users in various ways [ 4 ]. YouTube is the most widely (55%) used social media platform [ 3 ].

The terminology of addiction is generally used physiologically in the literature. According to DSM-IV, pathological use/abuse of any substance or stimulant is not defined as… addiction.” Instead, the concept of internet addiction is described as the “problematic/pathological use of the internet” [ 5 ]. While in the DSM-IV substance use disorder was broken into two separate diagnoses of substance abuse and substance dependence, in the new revision, DSM-5, they combined these two diagnoses into one, to create a single diagnostic category of substance use disorder. Symptoms of internet addiction, and hence social media addiction, which is a sub-category of internet addiction, can be listed as the following: an increasing and more frequent use of the internet, lying about the amount and duration of the use, constantly having an engaged mind with the internet and its elements, using the internet to avoid problems, and demonstrating continued usage while knowing the consequences of excessive use of the internet. Other behavioral effects consist of physical and mental disorders, increasing stagnation, insomnia, and anger. Accordingly, social effects could result in loss of occupational and leisure activities as well as social isolation [ 6 , 7 , 8 ].

Increased use of technology and tools like the internet, social media, smartphones, and digital games in daily life may cause addictions with technology that can result in depression. Numerous studies show that these specific addiction types are positively associated with depression [ 9 , 10 , 11 , 12 ]. Social media addiction can be defined as a type of psychological dependence that develops through cognitive, sensual, and behavioral processes and produces social, academic, or professional negative results in an individual’s life [ 13 , 14 ]. The DSM-5 underlines it as a common and serios medical illness. Excessive or problematic use of the internet and social media is defined as spending at least 8.5 h per week on it. Spending 21.5 h online per week with internet and social media usage is considered an addiction [ 15 , 16 ]. Internet addiction can lead to serious social and psychological problems and deterioration in individuals’ lives due to excessive and uncontrolled use of social media tools [ 17 ]. However, there are various difficulties in identifying the internet’s use as an addiction [ 7 ]. Children and young people exist in a world where the internet and social media are readily available, and they have the skill set for using these tools effectively. The researchers on social media addiction have reached the following significant findings in this age group: Individuals born in a world with no internet and then learn how to use it have become addicted to social media at a level far beyond those who were born into the internet [ 18 ].

The World Health Organization (WHO) emphasizes addiction, regardless of the kind, as a common mental health disorder in which persistent sadness is accompanied by loss of interest in activities that one usually enjoyed, in addition to a situation that accompanies the inability to perform daily activities for at least two weeks [ 19 ]. Social media addiction, being common among children, young people, and adults, can lead to changes in individual and social areas [ 20 ]. Ozturk (2002) describes depression as a condition with a deep, sometimes sad, and depressed mood. The depression symptoms are observed in individuals who spend a lot of time on social media and, in particular, in young people [ 21 , 22 ]. The transition from adolescence to adulthood is a period in which the individual is in a state of confusion given the search for identity. In addition to these symptoms, depression is described as a mood in which the individual has feelings and thoughts such as worthlessness, weakness, reluctance, pessimism, or guilt [ 19 , 23 ]. In socio-demographic variables, it is reported that depression occurs in early adulthood (late 20 s) in age-related studies [ 24 ].

In addition, internet addiction occurs in individuals with depression because of psychological, physical, and social reasons [ 25 ]. As a result, adolescents aged 10 to 19 experience sudden changes in their emotions, thoughts, and social relationships. On the other hand, adults experience social withdrawal, a decrease in interest and activity, and deterioration in friendship relationships [ 26 ]. In addition, the lack of social support is significantly related to internet addiction. As such, individuals with depression are more likely to be on the internet for social support [ 11 ]. In addition, previous studies demonstrate that the trend for fulfilling the social support need is moving from the traditional way to virtual by using social media platforms, which destroys face-to-face social relationships [ 27 ].

2. Purpose of the Study

The aim of this study was to investigate the effects of social media addiction on the level of depression in adult individuals. For this purpose, the following research questions were sought:

I. What is the relationship between social media addiction and depression in adults?
II. What variables cause differing levels of addiction in social media use?
III. How are socio-demographic characteristics linked with both social media use and depression?
IV. Which social media platform is causing depression the most in adults?

The study population includes all individuals who were over the age of 18 in Turkey at the time of data collection. An age limitation was put on the online system in order to reach those people who are above 18. An online survey resulted in 486 participants living in different communities in Turkey. The data were collected via online questionnaire programs. When eliminating participants who left the questionnaires blank or gave invalid answers, the final number of participants was 419 ( n = 419).

3.2. Data Collection Tools

The Socio-demographic Questionnaire, Social Media Addiction Scale (SMAS), and Beck Depression Inventory (BDI), designed and used in the previous studies for the same purpose of collecting data from the participants, were used in the study. Likert-type scales were utilized in the survey, yielding a reliability of the Social Media Addiction Scale (SMAS) with Cronbach’s alpha coefficient = 0.971.

3.3. Socio-Demographic Information Form

In order to determine the socio-demographic characteristics of participants and to measure the effects of these variables on social media addiction and depression levels, a form prepared by the researchers consisting of 17 questions was developed to measure the variables of interest. In addition to the basic demographic characteristics such as participants’ age, gender, educational status, marital status, etc., the researchers aimed to measure variables such as how long participants have been using social media, for what purpose social media had been used, and the duration of daily use of social media.

3.4. Social Media Addiction Scale (SMAS)

The Social Media Addiction Scale (SMAS) was developed by Tutgun-Unal and Deniz (2015) to measure the social media addiction of university students [ 13 ]. The SMAS consists of 41 items, and scores were obtained from a 5-point Likert scale, which is graded with frequency expressions within the range of “Always (5)”,“Often (4)”,“Sometimes (3)”, “Rarely (2)” to “Never (1)”, indicating that the higher point value indicates more social media addiction [ 13 ]. A brief description of the factors that make up SMAS and the items included in these factors are as follows: items 1–12 in the measurement tool are related to the “Busy” dimension and measure the effect of social media in which a person is engaged; items 13–17 are related to the “Emotion and Regulation” dimension and measure social media’s influence on one’s emotions; items 18–22 are related to the “Repetition” dimension and measure the inability to control the use of social media and the repetition of usage of the platform; and items 23–41 are related to the “Conflict” dimension and measure the effect of social media on the negative consequences of a person’s life. The Cronbach alpha value of the scale was found to be highly reliable, with a value of 0.967 [ 13 ].

3.5. Beck Depression Inventory

The Beck Depression Inventory (BDI), developed by Aron T. Beck in 1961 and revised in 1978 and 1989 [ 28 ], is a self-rating scale with 21 items that measures characteristic attitudes and symptoms of depression. In the calculation of the inventory, the lowest score is “0” and the highest score is “3” for each question. Accordingly, the lowest score to be obtained from the inventory is “0” and the highest score is “63”. Based on the scale, a lower result means less depression and a higher result means more depression. The validity and reliability study of the inventory was done by Hisli in 1988, and the Turkish version of translated BDI was used for this study. The cut-off point has been determined as 17 for the Turkish version [ 28 ]. The Cronbach’s alpha for the inventory is 0.91. The inventory is designed for adults, and the scoring for the inventory is a 4-point Likert scale.

3.6. Data Collection and Analysis

Easy and snowball sampling methods were used as data collection methods. Close and accessibility are preferred. Data collection tools were combined in a single questionnaire and presented to the participants through an online platform. The questionnaire was created on Google Forms, and the link for the survey was distributed to individuals in different cities. Participation took place on a voluntary basis. Total time to complete the survey was about 5–6 min for each participant. Data gathering took place over a span time of 30 days. The total number of participants was 486; however, due to some invalid or missing data, 67 participants were eliminated from the sample group, and 419 participants was the final total sample used.

Upon analysis of the data, it was determined to be distributed normally. Therefore, correlation, independent sample t-test, and One-Way ANOVA analyses were applied to the data, and results were interpreted. Data analysis was performed through the IBM SPSS Statistics 20 package program.

4.1. Socio-Demographic Features of Participants

A total of 29.4% of the participants in the study were men ( n = 123) and 70.4% were women ( n = 295). The ages of the participants ranged between 18 and 62, and the average age of the sample was 28.46. The highest level of education of the participants was a follows: 1.4% primary school ( n = 6), 9.8% secondary education ( n = 41), 50.1% undergraduate ( n = 210), and 38.7% graduate ( n = 162). A total of 77.3% of the participants ( n = 324) spoke a second language. When looking at their marital status, 66.6% were single ( n = 279), 31% ( n = 130) were married, 2.4% ( n = 10) were divorced/widowed, and 26% ( n = 109) of participants had children. A total of 10.5% ( n = 44) of participants lived alone; 47% ( n = 197) lived with extended family; 30.1% ( n = 126) lived with their spouse; 12.4% ( n = 52) lived with a roommate. Considering the employment status of the participants, 42.5% ( n = 178) were employed; 32.2% ( n = 135) were students; 21.5% ( n = 90) were unemployed; 2.9% ( n = 12) were housewives; and, 1% ( n = 4) were retired. Income status ranged between 0 and 15,000 with the average income of 2101 Turkish lira.

4.2. Information on Participants’ Use of Social Media

A total of 99.5% ( n = 417) of the participants were using social media, and 52% ( n = 218) were doing so between one and three hours per day, 20.5% ( n = 86) between four and six hours per day, 19.3% ( n = 81) for less than one hour per day, and 8.1% ( n = 34) for more than seven hours per day. Participants had been using social media for an average of eight years. It was determined that 50.4% ( n = 211) preferred Instagram, and 23.9% ( n = 100) preferred Facebook as social media platforms. When considering the objectives of participants’ social media use, 35.1% ( n = 147) reported using it for getting information, 25.8% (108) for leisure time activities during their free time, and 32.5% ( n = 136) for entertainment purposes such as online gaming.

4.3. Investigation of the Effect of Social Media Addiction on Depression

When the depression levels of the participants were examined, the ratio of those with minimal depression was 39.9%; 29.8% had mild depression; 21.5% had moderate depression; and the rate of those with severe depression was 8.8% (See Table 1 ).

Participants’ Level of Depression.

Beck Depression Inventory Levels	N	Percentage
Minimal Depression (0–9)	167	39.9
Mild Depression (10–16)	125	29.8
Moderate Depression (17–29)	90	21.5
Severe Depression (30–63)	37	8.8

When the BDI, SMAS and its subscales were statistically analyzed in terms of gender, there was no statistically significant difference between male and female participants in terms of Busyness, Emotion, Repetition, Conflict subscales and Social Media Addiction ( p > 0.05) (See Table 2 ).

Investigation of depression and social media addiction subscales in terms of gender (t-Test).

Variables	Gender	n	Average	Ss	t
BDI Score	Man	123	12.504	10.635	1.346	0.180
BDI Score	Woman	295	14.040	10.646	1.346	0.180
SMAS Total Score	Man	123	79.390	31.545	2.717	0.007
SMAS Total Score	Woman	295	88.633	32.068	2.717	0.007
Busyness	Man	123	32.647	10.594	3.713	0.180
Busyness	Woman	295	28.349	11.227	3.713	0.180
Emotion	Man	123	20.569	5.333	2.743	0.007
Emotion	Woman	295	23.308	9.400	2.743	0.007
Repetition	Man	123	8.926	5.333	2.339	0.020
Repetition	Woman	295	10.223	5.094	2.339	0.020
Conflict	Man	123	30.167	14.368	1.446	0.150
Conflict	Woman	295	32.857	14.589	1.446	0.150

BDI Scores: Lowest depression = 0, Highest depression = 63.

There was a weak positive correlation between the social media usage time and the subscales: Busyness ( r = 0.140), Conflict ( r = 0.119) Emotion ( r = 0.127); and a weak positive correlation ( r = 0.143) between SMAS. (See Table 3 ).

Investigation of SMAS and Its Sub-scales in Terms of Social Media Usage Time (Years).

		Busyness	Emotion	Repetition	Conflict	SMAS	BD
Usage Time (year)		0.140 **	0.127 **	0.059	0.119 *	0.143 **	0.088
		0.004	0.009	0.225	0.015	0.003	0.074
	n	418	418	418	418	418	418

p * < 0.05; p ** < 0.01.

There was a negative correlation between the number of children and the score of Busyness ( p ** <0.01) ( r = 0.210); a weak negative correlation between the Emotion score ( p < 0.05) ( r = 0.116); a weak negative relationship ( r = 0.150) between the SMAS ( p ** < 0.01), as well as a weak negative relationship ( r = 0.101) between Conflict dimension ( p * < 0.05) as well (See Table 4 ).

Investigation of the Relationship between SMAS, its Sub-scales, and Number of Children.

		Busyness	Emotion	Repetition	Conflict	SMAS	BD
Number of Children		−0.210 **	−0.116 *	−0.091	−0.101 *	−0.150 **	−0.091
		0.000	0.018	0.064	0.040	0.002	0.064
	n	418	418	418	418	418	418

p ** < 0.01 p * < 0.05.

There was a moderate positive correlation between BDI and Busyness (r = 0.363) and Repetition dimension (r = 0.333) a medium positive relationships between Emotion (r = 0.464); Conflict (r = 0.487); and a positive medium relationship between SMAS (r = 0.484) (See Table 5 ).

Investigation of the Relationship Between Depression Inventory and SMAS and its Sub-scales.

		Busyness	Emotion	Repetition	Conflict	SMAS
BDI	r	0.363	0.464	0.335	0.483	0.484
		0.000	0.000	0.000	0.000	0.000
	n	419	419	419	419	419

p ** < 0.01.

4.4. ANOVA Test Results with Multiple Variables

There is a significant relationship between the participants’ ages and SMAS and its sub-scales ( p < 0.05), as well as with the BDI scores ( p < 0.05). When the average scores are analyzed, it is seen that the age group with the highest social media addiction has a range of 18–25. There are also significant differences between the ages and depression levels of the participants ( p < 0.05). Depression scores are higher in the 18–33 age group, while depression scores are lower in the people over 34. Tukey HSD analysis was completed to determine where differences occurred. Those between the ages of 18–25 and those between the ages of 26–33 were found to differ (See Table 6 ).

Investigation of Depression and SMAS and its Sub-scales in terms of Age (ANOVA).

Variables	Age		Average	Ss	F
1BDI	18–25	197	14.3	11	3.39	0.01 *
	26–33	138	14.4	10.4
	34–41	78	10.2	8.9
	Over 42	6	11.1	14.5
SMAS Total Score	18–25	197	92	32.7	8.04	0.00 *
	26–33	138	85.9	31.4
	34–41	78	72.5	27.4
	Over 42	6	65.6	28.7
Busyness	18–25	197	33.9	11.1	10.75	0.00 *
	26–33	138	31	10.6
	34–41	78	26.1	10.0
	Over 42	6	23.8	12.9
Emotion	18–25	197	23.9	9.5	6.12	0.00 *
	26–33	138	22.7	9.1
	34–41	78	18.8	8.7
	Over 42	6	18.6	9.7
Repetition	18–25	197	10.6	5.5	4.59	0.003 *
	26–33	138	9.8	5.2
	34–41	78	8.2	4.4
	Over 42	6	6.8	4
Conflict	18–25	197	34.3	15.4	4.99	0.002 *
	26–33	138	32.2	14.5
	34–41	78	27.5	10.7
	Over 42	6	23.1	7.4

* p < 0.05.

As a result of the Anova test applied ( Table 7 ), there was significant difference between the participants’ depression level and marital status ( p = 0.007 <0.05). When looking at the sub-scales with SMAS, there is a significant difference between the marital status of the participants and the total score of SMAS ( p = 0.02 <0.05) and the Busyness ( p = 0.00 <0.05). Tukey HSD analysis was completed to determine for which groups there existed this difference, and it was found that it differs from the participants who are married and those who are single.

Investigation of Depression and SMAS and its Sub-scales in Terms of Marital Status (ANOVA).

Variables	Marital Status		Average	ss	F
SMAS Total Score	Single	279	80.49	30.2	2.90	0.002 *
	Married	130	80.38	24.8
	Divorced/Widowed	10	91.21	33.7
		419	81.64	31.8
BDI	Single	279	14.34	0.64	5.01	0.007 *
	Married	130	11.46	0.86
	Divorced/Widowed	10	19.70	3.58
		419	13.57	0.51
Busyness	Single	279	33.11	10.97	10.26	0.000 *
	Married	130	27.84	10.87
	Divorced/Widowed	10	30.40	12.13
		419	31.41	11.21
Emotion	Single	279	23.44	9.34	4.59	0.011 *
	Married	130	20.46	9.30
	Divorced/Widowed	10	23.70	10.22
		419	22.52	9.43
Repetition	Single	279	10.22	5.33	2.037	0.013 *
	Married	130	9.10	5.17
	Divorced/Widowed	10	9.50	5.10
		419	9.85	5.29
Conflict	Single	279	30.62	13.4	3.96	0.020 *
	Married	130	29.65	10.5
	Divorced/Widowed	10	33.38	15.5
		419	33.01	15.3

There was no significant relationship between participants’ depression, social media addiction, or sub-scales with educational level achieved ( p > 0.05) (See Table 8 ).

Investigation of Depression and SMAS and its Sub-scales in Terms of Educational Levels Achieved (ANOVA).

Variables	Educational Status		Average	ss	F
BDI	Primary school	6	15.16	18.8	0.69	0.55
	High School and Equivalent	41	15.2	13.5
	Associate Degree/Undergraduate	210	13.7	10.6
	Graduate (Master or Ph.D.)	162	12.8	9.4
SMAS Total Score	Primary school	6	90.5	51.9	0.21	0.88
	High School and Equivalent	41	88.7	33.7
	Associate Degree/Undergraduate	210	85.0	32.2
	Graduate (Master or Ph.D.)	162	86.4	31.1
Busyness	Primary school	6	30.8	18.1	0.54	0.64
	High School and Equivalent	41	30.8	11.7
	Associate Degree/Undergraduate	210	30.8	11.2
	Graduate (Master or Ph.D.)	162	32.3	10.8
Emotion	Primary school	6	24.8	14.3	0.25	0.85
	High School and Equivalent	41	23.1	10.3
	Associate Degree/Undergraduate	210	22.6	9.5
	Graduate (Master or Ph.D.)	162	22.1	8.9
Repetition	Primary school	6	10.0	7.5	0.01	0.99
	High School and Equivalent	41	9.78	5.8
	Associate Degree/Undergraduate	210	9.82	5.3
	Graduate (Master or Ph.D.)	162	9.91	5.02
Conflict	Primary school	6	34.8	21.3	0.53	0.65
	High School and Equivalent	41	34.7	13.5
	Associate Degree/Undergraduate	210	31.7	14.5
	Graduate (Master or Ph.D.)	162	32.0	14.5

As a result of the Anova test applied, there is a significant difference between the depression levels and income levels of the participants ( p = 0.03 <0.05). When looking at the sub-scales with SMAS, there is only a significant difference in the “Busyness” dimension of social media addiction on the participants ( p = 0.02 < 0.05). As the income levels of the participants increased, their busyness levels decreased (See Table 9 ).

Investigation of Depression and SMAS and its Sub-scales in Terms of Participants’ Income Levels (ANOVA).

Variables	Income		Average	ss	F
BDI Score	0–2000	247	14.63	11.2	2.867	0.036
	2100–4500	118	12.77	10.7
	4600–6500	36	11.41	5.1
	7000–15,000	15	8.13	8.2
SMAS Total Score	0–2000	247	89.29	32.6	2.450	0.063
	2100–4500	118	82.80	33.4
	4600–6500	36	79.19	25.3
	7000–15,000	15	74.46	23.4
Busyness	0–2000	247	32.61	10.9	3.284	0.020
	2100–4500	118	30.03	11.8
	4600–6500	36	29.77	10.9
	7000–15,000	15	25.40	7.8
Emotion	0–2000	247	23.13	9.5	1.249	0.291
	2100–4500	118	21.90	9.9
	4600–6500	36	21.50	7.8
	7000–15,000	15	19.26	6.9
Repetition	0–2000	247	10.23	5.4	1.764	0.153
	2100–4500	118	9.63	5.3
	4600–6500	36	8.69	4.5
	7000–15,000(	15	7.86	2.7
Conflict	0–2000	247	33.62	15.2	2.304	0.076
	2100–4500	118	30.87	14.5
	4600–6500	36	27.91	9.5
	7000–15,000	15	29.80	11.1

There is a significant relationship between participants’ working status, depression levels, social media addiction, and its sub-scales ( p < 0.05). Tukey HSD analysis was completed to determine for which groups there was a difference. We found a difference between employees, students, and job seekers (See Table 10 ).

Investigation of Relationship of Participants’ Employment Status with Depression, SMAS and its Sub-scales (ANOVA).

Variables	Employment Status		Average	ss	F
BDI Score	Employee	178	11.38	9.20	6.366	0.00 *
	Student	135	13.38	10.18
	Housewife	12	13.16	11.74
	Retiree	4	18.2	14.99
	Jobseeker	90	18.04	12.32
SMAS Total Score	Employee	178	79.92	30.11	3.811	0.004 *
	Student	135	90.58	31.71
	Housewife	12	76.08	33.2
	Retire	4	75.25	37.65
	Jobseeker	90	92.92	34.4
Busyness	Employee	178	29.18	11.17	4.525	0.001 *
	Student	135	33.41	10.40
	Housewife	12	27.16	12.58
	Retiree	4	26.0	9.66
	Jobseeker	90	33.63	11.46
Emotion	Employee	178	21.12	9.05	2.401	0.04 *
	Student	135	23.50	9.70
	Housewife	12	19.66	9.55
	Retiree	4	22.0	16.39
	Jobseeker	90	24.24	9.13
Repetition	Employee	178	9.00	4.79	3.03	0.01 *
	Student	135	10.79	5.71
	Housewife	12	7.75	5.25
	Retiree	4	9.25	7.84
	Jobseeker	90	10.45	5.24
Conflict	Employee	178	29.62	12.75	3.194	0.01 *
	Student	135	34.18	14.68
	Housewife	12	29.25	13.01
	Retiree	4	27.25	12.01
	Jobseeker	90	35.04	16.98

Working participants make up the group with the lowest score on the depression scale. The group with the highest score was job seekers and retirees. When social media addictions are compared, the group with the highest level of addiction is, again, job seekers, followed by students.

According to the Anova test result, there is no significant relationship between the depression levels of the participants and the social media platform they use most ( p = 0.22 > 0.05). There is a significant relationship between the social media addictions of the participants and the most used social media platform ( p = 0.02 <0.05). It is seen that Instagram users have higher addiction than those using other social media platforms. When looking at the sub-scales, there is no significant difference compared to the social media platform used in the “Repetition” and “Conflict” dimensions (See Table 11 ).

Investigation of Depression and SMAS and its Sub-scales on the Most Common Social Media Platforms (ANOVA).

Variables	The Most Common Platform		Average	ss	F
BDI Score	Facebook	100	13.73	11.77	1.43	0.22
	Twitter	44	11.68	10.02
	Instagram	211	14.57	10.51
	Youtube	59	11.49	9.46
	Other	3	10.66	9.8
SMAS Total Score	Facebook	100	80.49	30.2	2.90	0.02 *
	Twitter	44	80.38	24.8
	Instagram	211	91.21	33.7
	Youtube	59	81.64	31.8
	Other	3	73.33	27.3
Busyness	Facebook	100	28.34	10.1	7.25	0.00 *
	Twitter	44	29.63	9.5
	Instagram	211	34.21	11.6
	Youtube	59	28.16	9.9
	Other	3	27.33	12.6
Emotion	Facebook	100	21.53	9.1	2.72	0.02 *
	Twitter	44	21.09	7.9
	Instagram	211	23.95	9.6
	Youtube	59	20.45	9.3
	Other	3	17.33	6.4
Repetition	Facebook	100	9.57	4.9	1.10	0.35
	Twitter	44	9.68	5.07
	Instagram	211	10.31	5.5
	Youtube	59	8.98	5.3
	Other	3	7.00	2.6
Conflict	Facebook	100	33.38	13.4	1.06	0.37
	Twitter	44	30.62	10.5
	Instagram	211	29.65	15.5
	Youtube	59	33.01	15.3
	Other	3	28.66	9.5

* p < 0.05

There is a statistically significant relationship between the time spent by the participants on daily social media and depression, social media addiction, and its sub-scales ( p = 0.00 <0.05). This indicates that social media addiction and depression increase with the increase in time spent on social media. Tukey HSD analysis was completed to determine for which groups this difference existed, and it was observed that there was a differentiation among all groups (See Table 12 ).

Investigation of Depression and SMAS and its Sub-scales in Terms of Daily Time on Social Media (ANOVA).

Variables	Daily Time on Social Media (hr)		Average	ss	F
BDI Score	Less than 1	81	10.64	10.6	11.5	0.00 *
	1–3	218	12.17	9.5
	4–6	86	17.62	11.2
	7 or more	34	19.29	11.3
SMAS Total Score	Less than 1	81	62.85	19.6	50.2	0.00 *
	1–3	218	81.97	27.8
	4–6	86	104.79	28.6
	7 or more	34	119.41	38.2
Busyness	Less than 1	81	21.85	7.7	69.0	0.00 *
	1–3	218	30.13	9.3
	4–6	86	38.95	9.2
	7 or more	34	43.35	11.01
Emotion	Less than 1	81	16.48	6.4	37.0	0.00 *
	1–3	218	21.38	8.5
	4–6	86	28.25	8.8
	7 or more	34	29.79	10.01
Repetition	Less than 1	81	6.98	3.1	19.3	0.00 *
	1–3	218	9.57	4.9
	4–6	86	12	5.6
	7 or more	34	13.11	6.6
Conflict	Less than 1	81	24.53	7.9	28.6	0.00 *
	1–3	218	30.54	12.7
	4–6	86	38.18	15.06
	7 or more	34	46.26	20.2

Based on the Anova test applied, results indicate a significant difference between the participants’ aims of using social media, depression, social media addiction, and its sub-scales ( p < 0.05). Participants who responded that they used social media to spend their free time had the highest depression rate. The lowest depression score was for people who used social media for information. People who used social media for work had the second-highest depression score. When the SMAS scores were examined, those who used social media to spend their free time had higher addiction scores than those who used it for other purposes. People who used it for information had low SMAS scores as well as depression scores (See Table 13 ).

Investigation of Depression and SMAS and its Sub-scales in Terms of The Purpose of Social Media Use.

Variables	The Purpose of Social Media Use		Average	ss	F
BDI Score	To share my activities	18	12.3	9.7	4.8	0.00 *
	For entertainment purposes	136	14.3	10.9
	For information	147	10.7	8.69
	To spend my free time	108	16.3	12.01
	For my business	10	15.4	10.10
SMAS Total Score	To share my activities	18	81.6	36.3	7.83	0.00 *
	For entertainment purposes	136	91.8	35.4
	For information	147	75.1	25.7
	To spend my free time	108	94.5	31.6
	For my business	10	80.2	26.3
Busyness	To share my activities	18	30.3	12.1	10.24	0.00 *
	For entertainment purposes	136	33.5	11.4
	For information	147	27.1	9.51
	To spend my free time	108	34.9	11.2
	For my business	10	28.8	8.5
Emotion	To share my activities.	18	21.8	11.2	5.96	0.00 *
	For entertainment purposes.	136	23.7	10
	For information.	147	19.7	8.05
	To spend my free time.	108	24.9	9.2
	For my business.	10	21.9	10.3
Repetition	To share my activities.	18	9.9	5.6	2.30	0.05
	For entertainment purposes	136	10.4	5.6
	For information	147	8.8	4.5
	To spend my free time	108	10.5	5.6
	For my business	10	9.6	5.08
Conflict	To share my activities.	18	29.3	14.5	5.14	0.00 *
	For entertainment purposes	136	34.7	17.1
	For information	147	28.3	10.6
	To spend my free time	108	35.1	14.7
	For my business	10	29.5	9.6

5. Discussion

This study is an investigation on the relationship between adults’ depression and social media addiction, and the sub-problems mentioned in the aims were analyzed through various statistical methods. In the study, the numerical distribution of the socio-demographic characteristics of the participants was examined. Accordingly, 29.4% of the participants in the study were men ( n = 123) and 70.4% were women ( n = 295). When the effect of social media addiction and its sub-scales on depression was examined, there was no statistically significant difference between male and female participants in terms of Busyness, Emotion, Repetition, Conflict subscales, and Social Media Addiction ( p > 0.05). In a study conducted by Kirik et al. [ 29 ] on social media addiction with 271 undergraduates, no significant difference was found in terms of gender either. There are also studies in the literature showing that social media addiction of men is higher than in women [ 30 , 31 , 32 ]. Participants in the current study were using social media for an average of eight years. As a result of the correlation analysis, it was determined that there was a positive weak correlation with SMAS total score and the Busyness and Conflict sub-dimensions ( p < 0.05). In line with these results, it can be said that the participants who were using social media for a long time had high social media addiction scores and that social media had a large place in their daily lives and negatively affected their lives. It was noted that 26% ( n = 109) of the participants in the study have children, and the number of their children is between one and nine.

As a result of the correlation test, it was seen that there is a negative relationship between the number of children a participant has and Busyness, Emotion, Conflict sub-scales, and social media addiction ( p <0.05). Accordingly, it is possible to say that as the number of children increases, social media addiction decreases. Since this situation increases the responsibilities of the mother or father who cares for the child, there will be a direct decrease in the time allocated to social media and social media that is prevented from creating a negative impact on the individual’s life. There is a positive relationship between depression and social media addiction and the Business, Emotion, Repetition, and Conflict sub-scales. Accordingly, when social media addiction, Occupation, Emotion, Repetition, and Conflict sub-scales increase, the level of depression will increase in parallel. Studies in the literature indicate that as the level of internet usage and addiction increases, depression increases [ 11 , 33 ].

There is a significant relationship between the participants’ age group and SMAS and its sub-scales ( p < 0.05). This result overlaps with the results obtained by Kirik et al. [ 29 ]. As a result of the Anova test, it was found there was a significant difference between the participants’ depression level and marital status ( p < 0.007). When looking at the sub-scales with SMAS, there was a significant difference between the marital status of the participants and the SMAS total score ( p < 0.05) and the Busyness sub-scale. In this respect, it could be said that married individuals have less time to spend than single individuals in social media because of the increase in their family responsibilities.

There is a significant relationship between participants’ working status, depression levels, social media addiction, and its sub-scales ( p < 0.05). According to the results, it could be argued that individuals who work spend less time on social media than students or job seekers, and it varies depending on the job conditions. The results also indicate the retiree group has the least tendency toward online addiction, scoring the lowest for the total SMAS total score, Busyness, and Conflict, yet they had the highest BDI score. Besides, it was determined that 50.4% ( n = 211) of participants preferred Instagram and 23.9% (n = 100) preferred Facebook.

Additionally, there is a significant relationship between the social media addictions of the participants and the most used social media platform ( p < 0.05). Instagram users were found to have higher dependence than those using other social media platforms. In parallel with this result, according to Turan’s [ 34 ] study on undergraduates, a statistically significant difference was found between the students’ internet addiction scale score and the most used social media platforms. A total of 52% of the participants ( n = 218) spent one to three hours a day on social media.

In addition, there is a statistically significant relationship between the time spent by the participants on daily social media and depression, social media addiction, and its sub-scales ( p < 0.05). This indicates that social media addiction and depression increase as daily social media usage time increases. In other words, it can be said that internet usage time increases social media addiction. Conversely, participants used more daily internet due to their social media addiction. This addiction-related result overlaps with the results obtained by Kirik et al. [ 29 ].

In a study conducted by Balci and Ayhan [ 35 ], the factors affecting internet use of undergraduates are specified as a social escape, information acquisition, recreation, economic benefit, social interaction, and entertainment. Another study conducted by Altayef [ 32 ] found a significant difference only in the course preparation dimension of the scale of the social media usage objectives and stated that, as the age decreases, the students used social media more for the preparation of lessons. According to a study conducted by Ak et al. [ 36 ], there is a significant relationship between the use of social media and online gaming and the level of internet addiction. In conclusion, the risk of being internet addicted is significantly related to the use of social media.

6. Conclusions

Increased use of technology and tools like the internet, social media, smartphones, and digital games in daily life has a crucial role in addictions with technology that can result in depression. This study proves that those who were using social media for a long time had high social media addiction scores. In other words, social media had a large place in their daily lives and negatively affected their lives. With the internet becoming an indispensable part of human life, social media addiction is likely to increase gradually. Excessive use of social media tools poses various risks in terms of psychological, physical, and social aspects, in addition to causing problems in the social functionality of individuals. The group most at risk to experience social media addiction are adolescents. It is a necessary to consider differential health policies in relating to the functional change of the “digital age”. Taking into consideration the changing social problems of public policies, it is useful to prepare action plans for behavioral addiction types such as social media addiction, game addiction, and internet addiction, which have been frequently encountered in recent years. It will be necessary to implement such action plans as soon as possible. In order to facilitate the diagnosis and treatment processes of these types of behavioral addictions, relevant rehabilitation and diagnosis centers should be established. At the same time, the lack of social support and the lack of social environment in the orientation of individuals to social media tools causes such addictions. Therefore, family group studies, family therapy, and seminars should be organized to strengthen family dynamics.

Author Contributions

Data curation, M.Z.Y.; Formal analysis, T.A.S. and B.B.; Resources, B.B. and E.Z.A.; Writing–original draft, S.A. and O.K.; Writing–review & editing, S.A., O.K., T.A.S. and E.Z.A. All authors have read and agreed to the published version of the manuscript.

This research received no external funding.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

Conflicts of interest.

The authors declare no conflict of interest.

Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Exploring the Association Between Social Media Addiction and Relationship Satisfaction: Psychological Distress as a Mediator

Original Article
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Published: 05 October 2021
Volume 21 , pages 2037–2051, ( 2023 )

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Begum Satici ORCID: orcid.org/0000-0003-2161-782X 1 ,
Ahmet Rifat Kayis ORCID: orcid.org/0000-0003-4642-7766 2 &
Mark D. Griffiths ORCID: orcid.org/0000-0001-8880-6524 3

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Social media use has become part of daily life for many people. Earlier research showed that problematic social media use is associated with psychological distress and relationship satisfaction. The aim of the present study was to examine the mediating role of psychological distress in the relationship between social media addiction (SMA) and romantic relationship satisfaction (RS). Participants comprised 334 undergraduates from four mid-sized universities in Turkey who completed an offline survey. The survey included the Relationship Assessment Scale, the Social Media Disorder Scale, and the Depression Anxiety and Stress Scale. According to the results, there were significant correlations between all variables. The results also indicated that depression, anxiety, and stress partially mediated the impact of SMA on RS. Moreover, utilizing the bootstrapping procedure the study found significant associations between SMA and RS via psychological distress. Consequently, reducing social media use may help couples deal with romantic relationship dissatisfaction, thereby mitigating their depression, anxiety, and stress.

A two-study validation of a single-item measure of relationship satisfaction: RAS-1

Effects of attachment styles, dark triad, rejection sensitivity, and relationship satisfaction on social media addiction: A mediated model

Avoid common mistakes on your manuscript.

Establishing social relationships is one of the basic needs of human beings (Heaney & Israel, 2008 ). How this basic need is met can vary greatly. In particular, technological developments, such as computers, the Internet, and smartphones have created new ways for people to communicate with each other. One of the most successful new means of communication is through social media. Social media involves many different communication (i.e., social networking) platforms. Among the most popular are platforms in Western countries are Facebook, Twitter, Instagram, and YouTube. These sites, which are accessed via the Internet, provide many opportunities for communication, such as voice and video messaging, photograph and video sharing, and creating profiles, through which individuals can introduce themselves and make connections with others.

The communication opportunities brought about by social networking sites (SNSs) allow for the development of social relationships (Fuchs, 2017 ; Hazar, 2011 ; Valentini, 2015 ). In addition, social media is used for a wider variety of purposes, including obtaining information, communicating, entertainment, playing games, and sharing photos, videos, and music (Griffiths, 2012 ). However, excessive use of social media including SNSs can cause negative effects (Griffiths, 2013 ; van den Eijnden et al., 2016 ). This phenomenon, which is sometimes referred as “social media addiction,” is defined as the irrational and excessive use of social media at a level that negatively affects the daily life of the user (Griffiths, 2012 ). When social media use reaches the level of addiction, it can prevent the establishment of real, face-to-face social relationships (Glaser et al., 2018 ; Kuss & Griffiths, 2017 ; Young, 2019 ). When general characteristics of social media addiction have been examined, it has been found that individuals tend to have restless thoughts concerning the urges and craving to be on social media, lose their self-control over their use of social media, spend excessive amounts of time staying on (or thinking about) social media which in turn lead to negative impacts on their relationships with their families and friends, and compromise their occupation and/or education (Andreassen et al., 2012 ; Griffiths et al., 2014 ). Therefore, examining social media addiction in terms of its effect on human relationships and mental health is an important pursuit.

Theoretical Framework

Social media addiction and relationship satisfaction.

Research into the effects of social media addiction on romantic relationships has increased (Abbasi, 2019a ; Demircioğlu & Köse, 2018 ). The literature suggests that social media addiction negatively affects romantic relationships due to its tendency to create jealousy and suspicion and facilitate deception between married couples and committed partners (Abbasi, 2019b ). Additionally, problematic social media use can hinder the development of face-to-face relationships (Glaser et al., 2018 ; Kuss & Griffiths, 2017 ; Pollet et al., 2011 ; Young, 2019 ). Therefore, it is possible that some couples’ relationships may become disrupted and that dissatisfaction may be experienced. In some cases, not only has social media use decreased the amount of relationships that individuals have in person, but it has also markedly impaired the quality of the time spent together. Therefore, it can be concluded that some couples may experience relationship dissatisfaction.

Similarly, social media addiction can result in low relationship satisfaction due to the existence of online alternative centers of attraction and investments of time and emotion outside the bilateral relationship in individuals aged between 18 and 73 years (Abbasi, 2019a ). In addition, social media addiction has also been associated with physical and emotional infidelity, romantic separation, decline in the quality of romantic relationships, and relationship dissatisfaction (e.g., Abbasi, 2019a , b ; Demircioğlu & Köse, 2018 ; Valenzuela et al., 2014 ). Therefore, these aforementioned findings indicate that social media addiction negatively affects relationship satisfaction.

Social Media Addiction and Psychological Distress

One of the most important consequences of social media addiction is the mental health of individuals. When social media use reaches the level of addiction, it can create stress and negatively affect mental health rather than being a method of healthy coping. This occurs because social media addiction triggers social media fatigue and, as a result, individuals may experience anxiety and depression (Dhir et al., 2018 ). Social media users may use social media as a means of diversion in order to cope with stress (van den Eijnden et al., 2016 ). However, social media addicts give a lower priority to hobbies, daily routines, and close relationships (Tutgun-Ünal & Deniz, 2015 ) which in turn lead to problems with daily functioning, completion of tasks, and relationship maintenance. This puts such individuals at risk for experiencing negative physical and psychological health.

In fact, some research has claimed that social media addiction triggers psychological distress factors, such as depression, anxiety (Woods & Scott, 2016 ), and stress (Larcombe et al., 2016 ). In addition, a meta-analysis synthesizing the findings of 13 studies found that social media addiction may increase depression, anxiety, and stress levels (Keles et al., 2020 ). In both meta-analyses and cross-sectional studies, it has been found that social media addiction can increase psychological distress (e.g., Hou et al., 2019 ; Keles et al., 2020 ; Marino et al., 2018 ; Meena et al., 2015 ). In sum, these findings consistently associate social media addiction with psychological distress.

Psychological Distress and Relationship Satisfaction

Individuals experiencing psychological discomfort often have non-functional communication styles characterized by highly negative behaviors, such as criticism, complaining, hostility, defensiveness, and tendency to end relationships. They also experience problems actively listening to others (Fincham et al., 2018 ). In this respect, psychological distress prevents healthy communication in relationships, and a lack of healthy communication may cause conflicts that can embitter psychological distress between couples. Such a situation can continue in a cyclical manner that prevents relationship satisfaction. In romantic relationships, couples are supposed to fulfill their partners’ emotional needs (Willard, 2011 ). When individuals have psychological problems due to social media addiction, they will ignore their partner’s emotional needs because they would be trying to deal with their own problems, which, in turn, may lead to lower relationship satisfaction.

When psychological distress and romantic relationship satisfaction are examined, it can be seen that much psychological distress, such as major depression, panic disorder, social phobia, general anxiety disorder, post-traumatic stress disorder, and mood disorder, positively predict relationship dissatisfaction (Whisman, 1999 ). On the other hand, it can also be seen that individuals who are sensitive to negative affect in romantic relationships and who can successfully stop these emotions early on and cope with their feelings are satisfied with their relationships (Fincham et al., 2018 ).

Couples who have high levels of stress are reported to experience less satisfaction in their relationships (Bodenmann et al., 2007 ). In addition, it is known that depression negatively predicts relationship satisfaction (Cramer, 2004a , b ; Tolpin et al., 2006 ). Therefore, it appears that psychological distress negatively affects relationship satisfaction.

The Present Study

The prevalence of the use of the internet and Internet-related tools has consistently increased year on year (Roser et al., 2020 ). Even though the social media use is widespread and facilitates communication when it is used normally, it can negatively affect daily life when it is used excessively by some individuals. Literature reviews have shown that social media addiction has been mostly studied in East Asian countries like China, Japan, and South Korea (e.g., Bian & Leung, 2015 ; Kwon et al., 2013 ; Tateno et al., 2019 ). In this respect, when the prevalence of social media use among Turkish people and the different cultural context of the present study are considered, the findings would arguably make important contributions to the current literature. Furthermore, the present study appears to be the first to examine the mediating role of psychological distress in the relationship between social media addiction and romantic relationship satisfaction.

Older aged adolescents and emerging adults are inextricably connected with technology in terms of their social media use and stand out as an important risk group in relation to problematic social media use (Griffiths et al., 2014 ). Many young adults closely follow technological developments and often adopt every innovation that arises into their lives without wasting time (Kuyucu, 2017 ). When such use becomes problematic, some individuals experience serious difficulty in maintaining their mental health. For example, cross-sectional studies among adolescents (Woods & Scott, 2016 ) and young adults (Larcombe et al., 2016 ) have found that social media addiction can lead to stress, anxiety, and depression. Moreover, the establishment of close relationships as a young adult is an important stage of emotional and social development (Cashen & Grotevant, 2019 ; Orenstein, & Lewis, 2020 ). Romantic relationship satisfaction may be seen as an important indicator of young people’s ability to engage in intimacy in a healthy manner (Orenstein & Lewis, 2020 ). Therefore, the findings obtained as a result of examining the relationships between social media addiction, psychological distress, and romantic relationship satisfaction among young people will contribute to an understanding of the associations between the psychological and social variables regarding maintenance of their mental health and their success in establishing close relationships.

In previous studies of the variables examined in the present study, even though studies examining the three variables dichotomously have been conducted (e.g., Abbasi, 2019a , b ; Bodenmann et al., 2007 ; Keles et al., 2020 ; Larcombe et al., 2016 ; Whisman, 1999 ), no research examining social media addiction, psychological distress (depression, anxiety and stress), and romantic relationship satisfaction together has been published. In particular, there is no study examining the role of psychological distress mediating between social media addiction and relationship satisfaction. In this respect, the results of the present study may also allow the findings of previous studies (which have been conducted with the aim of identifying the relationship between these variables) to be evaluated from a wider perspective.

Consequently, given the aforementioned theoretical explanations and the research findings, it has been demonstrated that social media addiction appears to induce both psychological distress and a low level of romantic relationship satisfaction (e.g., Demircioğlu & Köse, 2018 ; Woods & Scott, 2016 ). This is due to the deterioration of individuals’ mental health that can arise as a result of social media addiction (Baker & Algorta, 2016 ; Dhir et al., 2018 ), and in contrast to the advantages of developing relationships, it can lead to romantic relationship dissatisfaction (Abbasi, 2019b ; Muise et al., 2009 ). Therefore, when the relationships between social media addiction, psychological distress, and romantic relationship satisfaction are evaluated simultaneously, psychological distress may represent a mediating variable between social media addiction and romantic relationship satisfaction. Consequently, it was hypothesized that psychological distress would mediate the association between social media addiction and relationship satisfaction.

Participants and Procedure

The present cross-sectional study was carried out on a convenience sample of university students from three universities that are located in the west, middle, and east part of Turkey. A total of 350 surveys were originally distributed. Of these, 16 participants were removed because of incomplete data, yielding a final sample of 334 participants aged between 18 and 29 years ( M = 20.71 years, SD = 2.18). The participants comprised 214 females (64%) and 120 males (36%), of which 90 were freshmen, 87 were sophomores, 84 were junior students, and 73 were senior students. Participants reported that they were currently in a romantic relationship and reported having an average of 3.21 romantic relationships to date ( SD = 2.21). Table 1 shows the detailed demographic characteristics of the participants. Written informed consent was obtained from the volunteer participants prior to participation in the study. Research participants were assured of the confidentiality of the collected data. Data collection was carried out through a “paper-and-pencil” survey in the classroom environment. The surveys took less than 15 min to complete.

Relationship Assessment Scale (RAS)

The RAS was designed to assess general relationship satisfaction (Hendrick, 1988 ). Items (e.g., “In general, how satisfied are you with your relationship?”) utilize a seven-point Likert scale ranging from 1 ( low ) to 7 ( high ). The total score ranges from 7 to 49. The higher the score, the higher the relationship satisfaction. Hendrick ( 1988 ) reported very good reliability. The RAS was adapted into Turkish by Curun ( 2001 ) with very good internal consistency. In the present study, the internal consistency of this scale was also good ( α = 0.80).

Social Media Disorder Scale (SMD)

The SMD was designed to assess overall social media addiction, and the items were developed by adapting the DSM-5 criteria for Internet gaming disorder (van den Eijnden et al., 2016 ). This scale includes nine items (e.g., “… regularly found that you can't think of anything else but the moment that you will be able to use social media again?”) to which participants indicate their level of agreement on a five-point Likert scale ranging from 0 ( never ) to 4 ( always ). The total score ranges from 0 to 36. The higher the score, the higher the risk of social media addiction. The SMD was adapted to Turkish by Savci et al. ( 2018 ) and has very good internal consistency. In the present study, the internal consistency of this scale was also very good ( α = 0.88).

Depression Anxiety and Stress Scale (DASS-21)

The DASS was designed to assess the level of psychological distress (Henry & Crawford, 2005 ). The scale consists of 21 items that are rated on a four-point Likert scale from 0 ( did not apply to me at all ) to 3 ( applied to me very much or most of the time ) and comprises three sub-scales: depression (seven items; e.g., “I found it difficult to work up the initiative to do things”), anxiety (seven items; e.g., “I felt I was close to panic”), and stress (seven items; “I found myself getting agitated”). The scores range from 0 to 21 for each sub-scale. The DASS-21 subscales’ scores were multiplied by two based on Lovibund and Lovibond’s ( 1995 ) suggestion to the cut-offs (see Appendix 1 ). The DASS-21 was adapted to Turkish by Yilmaz et al. ( 2017 ) with good to very good internal consistencies. In the present study, the internal consistency of the sub-scales were all very good ( α = 0.89, 0.82, 0.85, respectively).

Statistical Analyses

Pearson correlations, means, and standard deviations were examined as preliminary analyses for all study variables. To examine whether the association between social media addiction and relationship satisfaction was mediated by psychological distress, the mediation model was calculated using the PROCESS macro (model 4), developed by Hayes ( 2018 ). As recommended by Hayes ( 2018 ), all regression/path coefficients are in unstandardized form. A total of 10,000 bootstrap samples were generated and bias corrected 95% confidence intervals calculated.

Written informed consent was obtained from the volunteer participants prior to participation in the study. This research was approved by Artvin Coruh University Scientific Research and Ethical Review Board (REF: E.5375).

Descriptive Statistics

Bivariate Pearson correlations among study variables were investigated (see Table 2 ). As expected, social media addiction was significantly and positively correlated with depression, anxiety, and stress. There was a significant negative correlation between social media addiction and relationship satisfaction.

Results indicated that 156 participants had no depressive symptoms (46.7%), 54 participants had mild depressive symptoms (16.2%), and the remainder had depressive symptoms (16.5% moderate, 9.9% severe, and 10.8% extremely severe). Moreover, 101 participants had no anxiety symptoms (30.2%), 30 participants had mild anxiety symptoms (9.0%), and the remainder had anxiety symptoms (20.4% moderate, 15.6% severe, and 24.9% extremely severe). Finally, 163 participants had no stress symptoms (48.8%), 47 participants had mild depressive symptoms (14.1%), and the remainder had stress symptoms (17.7% moderate, 12.6% severe, and 6.9% extremely severe) (see Appendix 1 ).

Statistical Assumption Tests

Prior to mediation analysis, statistical assumptions were evaluated. Skewness and kurtosis values (> ± 2; George & Mallery, 2003 ) were checked for normality, and there were no violations (see Table 3 ). All reliability coefficients were above Nunnally and Bernstein’s ( 1994 ) 0.70 criterion. Multicollinearity was checked with variance inflated factor (VIF), tolerance, and Durbin-Watson (DW) value. The results showed that VIF ranged from 1.47 to 2.09 and tolerance ranged from 0.48 to 0.87. These findings also showed that there was no multiple linearity problem according to Field’s ( 2013 ) recommendation. Also, the DW value was 1.82 indicating no significant correlations between the residuals.

Mediation Analyses

Applying PROCESS model 4, the analysis assessed whether psychological distress mediated the relationship between social media addiction and relationship satisfaction (see Table 4 ; Fig. 1 ). The results showed a significant total direct effect ( path c ; without mediator) of social media addiction on relationship satisfaction (B = − 0.36, t (334) = − 4.74, p = 0.001, 95% CI = − 0.51, − 0.21), significant direct effect ( path c ; with mediator) (B = − 0.16, t (334) = − 2.11, p = 0.03, 95% CI = − 0.04, − 0.01), and a significant indirect effect via psychological distress (total B = − 0.20, 95% CI = − 0.29, − 0.12).

The mediation model. * p < .05. ** p < .001

The results also showed that the social media addiction was associated with higher depression scores (path a 1 ; B = 0.23, p = 0.001), anxiety scores (path a 2 ; B = 0.23, p = 0.001), and stress scores (path a 3 ; B = 0.27, p = 0.001), and these, in turn, were negatively associated with relationship satisfaction (path b 1, b 2, b 3 ; B = − 0.28, B = − 0.28, B = − 0.26, all p values < 0.05, respectively).

In contemporary society, rapidly developing technology has entered human life, but some individuals may have difficulty in adapting to the innovations brought by such technology. Consequently, some individuals may experience psychological and social problems. Social media use, which has markedly increased in the past decade, can cause psychological distress (e.g., Keles et al., 2020 ; Marino et al., 2018 ) and the deterioration of interpersonal relationships (e.g., Glaser et al., 2018 ; Kuss & Griffiths, 2017 ; Young, 2019 ) among a minority of individuals. In this context, the main purpose of the present study was to evaluate the mediating role of psychological distress in the relationship between social media addiction and romantic relationship satisfaction.

According to the findings, a high level of social media addiction leads to a decrease in relationship satisfaction. Consequently, the first hypothesis was confirmed. A recent study conducted by Abbasi ( 2019b ) found that social media addiction was negatively associated with romantic relationship commitment. In another recent study, it was emphasized that social media addiction results in deception between couples through social media and may lead to the deterioration of relationships as a consequence (Abbasi, 2019a ). In addition, social media addiction not only leads to physical and emotional deception but also appears to negatively impact on the quality of romantic relationships (Demircioğlu & Köse 2018 ; Valenzuela et al., 2014 ). Therefore, the findings obtained in the present study are in line with the findings of previous research.

In the study here, the findings showed that a high level of social media addiction appears to result in psychological distress. Dhir et al. ( 2018 ) argued that social media addiction triggers social media fatigue, leading to anxiety and depression. Similarly, social media addiction has been found to be associated with depression, anxiety (Woods & Scott, 2016 ) and stress (Larcombe et al., 2016 ). In addition, a recent meta-analysis also concluded that social media addiction is closely and positively associated depression, anxiety and stress (Marino et al., 2018 ). Therefore, the findings of the present study are consistent with previous research.

Thirdly, the findings indicate that individuals who experience psychological distress have a low level of satisfaction in their romantic relationships. Whisman ( 1999 ) found that psychological distress positively predicted relationship dissatisfaction. It has also been suggested that couples with high levels of stress experience dissatisfaction in their romantic relationships (Bodenmann et al., 2007 ). In addition, there have also been a number of studies which indicate that the relationship satisfaction of individuals with high levels of depression is low (Cramer, 2004a , b ; Tolpin et al., 2006 ). In this respect, the findings obtained from the present study are similar to the findings of the previous studies.

Within the scope of this study, it was hypothesized that psychological distress would mediate between social media addiction and relationship satisfaction. In this sense, the study showed that social media addiction predicted romantic relationship satisfaction, partially mediated by psychological distress. Consequently, the fourth hypothesis of the research was also confirmed. No previous studies have examined the effect of psychological distress in the relationship between social media addiction and relationship satisfaction. However, there are research findings which provide evidence that social media addiction predicts both psychological distress (e.g., Larcombe et al., 2016 ; Woods & Scott, 2016 ) and relationship dissatisfaction (e.g., Demircioğlu & Köse, 2018 ; Valenzuela et al., 2014 ) and that psychological distress predicts relationship dissatisfaction (e.g., Bodenmann et al., 2007 ; Whisman, 1999 ). Due to the consideration of a variable’s mediating conditions (Barron & Kenny, 1986 ), it may be asserted that the findings of the previous studies in the literature and the findings of this research are consistent. Furthermore, it has been demonstrated that technological addiction, such as Internet addiction and smartphone addiction, is associated with psychological distress (McNicol & Thorsteinsson, 2017 ; Samaha & Hawi, 2016 ; Young & Rogers, 1998 ). Psychological distress may also predict variables such as closeness in relationships (Manne et al., 2010 ), dating violence (Cascardi, 2016 ), and social support (Robitaille et al., 2012 ) which are based on interpersonal relationships. It is therefore suggested that there is similarity between these findings and the findings of the present study. Consequently, it may be that the results of the studies conducted previously support the findings of this the present research indirectly, if not directly.

In the study here, the mediating role of psychological distress in the relationship between social media addiction and romantic relationship satisfaction was investigated. However, there could be some other variables that can mediate the relationship between social media addiction and romantic relationship satisfaction. For instance, romantic relationships are considered interpersonal (Knap et al., 2002 ); therefore, it can be assumed that interpersonal relationships and communication skills can be seen as potential mediators of the relationship between social media addiction and romantic relationship satisfaction. Additionally, given that psychological problems are the indicators of poor mental health (American Psychiatric Association, 2013 ), it can be assumed that variables (i.e., other indicators of poor mental health such as burnout, somatization, and hostility) would mediate the relationship between social media addiction and romantic relationship satisfaction. Therefore, future studies should investigate such relationships more closely.

When the role of social media addiction in the development of psychological distress is considered, it is necessary for social media addiction to be included in the process of forming the content of the intervention programs that aim to treat psychological distress. As such, it is interesting that an intervention program aimed at decreasing the level of social media addiction was also found to have a beneficial impact on individuals’ mental health (Hou et al., 2019 ). Likewise, the treatment of couples’ social media usage habits in family and couple therapies may be effective in terms of the efficacy of the therapy, since social media addiction decreases satisfaction in romantic relationships. Moreover, given the mediation relationships in the present research, the results may provide a more holistic viewpoint for mental health professionals which consider all of the three variables (social media addiction, psychological distress, and romantic relationship satisfaction) rather than a focus on only one. In this context, the following suggestions are made: to prevent social media addiction, effective Internet use skills can be taught to couples. In addition, awareness-raising skills such as yoga and meditation could be provided to individuals to protect them from social media addiction and psychological distress.

In terms of the study’s participatory group, it is significant that social media addiction (Kittinger et al., 2012 ; Koc & Gulyagci, 2013 ), psychological distress (Canby et al., 2015 ; Larcombe et al., 2016 ), and relationship satisfaction problems (Bruner et al., 2015 ; Roberts & David, 2016 ) are frequently experienced by university students. Consequently, the findings of the present study may be of particular help to specialists who work in the psychological counseling centers of universities. Within this framework, meetings, conferences, and psycho-educational group activities could be carried out to improve relationship building skills, as well as activities preventing social media addiction and psychological distress.

The present study has some limitations. Firstly, the data comprised self-report scales, which may decrease internal reliability, a limitation which may be prevented through the use of different methods of data collection. Secondly, the generalizability of the findings is limited since the sample was based on convenience sampling. Thirdly, the research design was cross-sectional. This may make it difficult to explain the cause-effect relationship of variables in the study, and therefore, experimental and longitudinal studies are recommended in future research which should examine the relationship between these variables. Finally, only the mediating role of psychological distress was examined in the research. Other possible mediating variables were not examined.

In the present research, the mediation of psychological distress in the relationship between social media addiction and romantic relationship satisfaction was empirically tested. Results showed that social media addiction predicted the partial mediation of depression, anxiety, and stress on romantic relationship satisfaction. In other words, social media addiction apparently increased individuals’ depression, anxiety, and stress levels, and this situation decreased the level of satisfaction in individual’s romantic relationships. In the present study, psychological and social variables were examined simultaneously. Overall, this study suggests that social media addiction may have a meaningful but negative impact on romantic relationship satisfaction via depression, anxiety, and stress.

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Depression, Anxiety and Stress Scale: Cut-Off Criteria and Distribution of Participants

	Depression	Anxiety	Stress

Normal	0–9	0–7	0–14
Mild	10–13	8–9	15–18
Moderate	14–20	10–14	19–25
Severe	21–27	15–19	26–33
Extremely severe	28 +	20 +	34 +

Normal	156 (46.7%)	101 (30.2%)	163 (48.8%)
Mild	54 (16.2%)	30 (9.0%)	47 (14.1%)
Moderate	55 (16.5%)	68 (20.4%)	59 (17.7%)
Severe	33 (9.9%)	52 (15.6%)	42 (12.6%)
Extremely severe	36 (10.8)	83 (24.9%)	23 (6.9%)

Scores on the DASS-21 are multiplied by 2 to calculate the final score

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Satici, B., Kayis, A.R. & Griffiths, M.D. Exploring the Association Between Social Media Addiction and Relationship Satisfaction: Psychological Distress as a Mediator. Int J Ment Health Addiction 21 , 2037–2051 (2023). https://doi.org/10.1007/s11469-021-00658-0

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Social media use can bring negative effects to college students, such as social media addiction (SMA) and decline in academic performance. SMA may increase the perceived stress level of college students, and stress has a negative impact on academic performance, but this potential mediating role of stress has not been verified in existing studies. In this paper, a research model was developed ...
Full article: Social Networking Addiction Scale
The two measures that assess generalized social media addiction are Bergen Social Media Addiction Scale (Andreassen et al., Citation 2016) and Social media ... His Ph.D. thesis was in the area of Industrial and Organizational Psychology. He has worked on dark triads of personality in the organizational context. He has also explored modern ...
Addiction to Social Media and Attachment Styles: A Systematic
Web-based communication via social networking sites (SNSs) is growing fast among adolescents and adults and some research suggests that excessive SNS use can become an addiction among a small minority of individuals. There is a growing body of research that has examined the impact of attachment styles and its influence on internet addiction (more generally) and social media addiction (more ...
PDF The impact of social media on students' lives
hand, the overuse of social media causes addiction (Schou Andreassen & Pallesen 2014). Overusing social media affects academic performance; it reduces cognition, makes stu- ... fect students' academic performance so that students can use social media effectively. This thesis aims to explore the question of just what that impact is. 1.2 ...
Full article: The relationship between social media addiction and
Social media addiction was measured using Dr Kimberly Young's Internet Addiction Test (IAT) (Young, Citation 1998). Although IAT is designed for measuring internet addiction, we replicated it for measuring social media addiction. IAT consists of 20 items and all these items were answered on a five-point Likert scale (0 = not applicable, 5 ...
Social Media Use and Its Impact on Relationships and Emotions
effects of social media use on emotions. Seo, Park, Kim, and Park, (2016) found that a person. who had developed a dependency to their cell phone experienced decreased attention and. increased depression which led to a negative impact on their social relationships with their.
A review of theories and models applied in studies of social media
Among these terms, social media addiction (including its variations, such as, Facebook addiction, SNSs addiction, and addictive SNSs use) is most commonly used and is defined as a maladaptive psychological dependency on SNS to the extent that behavioral addiction symptoms occur (Cao et al., 2020, Chen, 2019, Turel and Serenko, 2012).
Investigation of the Effect of Social Media Addiction on Adults with
Accordingly, social effects could result in loss of occupational and leisure activities as well as social isolation [ 6, 7, 8 ]. Increased use of technology and tools like the internet, social media, smartphones, and digital games in daily life may cause addictions with technology that can result in depression.
PDF IMPACTS OF SOCIAL MEDIA ON MENTAL HEALTH
Title of Bachelor´s thesis: Impacts of Social Media on Mental Health . Supervisor: Ilkka Mikkonen . Term and year of completion: Autumn 2018 Number of pages: 36 . Social media has become an integral part of human beings in the present era. It has influenced them in many ways. On the one hand, numerous benefits of social media such as online ...
Frontiers
Introduction. Social media generally refers to third-party internet-based platforms that mainly focus on social interactions, community-based inputs, and content sharing among its community of users and only feature content created by their users and not that licensed from third parties ().Social networking sites such as Facebook, Instagram, and TikTok are prominent examples of social media ...
The Keep: Institutional Repository of Eastern Illinois University
The Keep is Eastern Illinois University's institutional repository, offering access to the university's academic research and publications.
Exploring the Association Between Social Media Addiction and ...
Social media use has become part of daily life for many people. Earlier research showed that problematic social media use is associated with psychological distress and relationship satisfaction. The aim of the present study was to examine the mediating role of psychological distress in the relationship between social media addiction (SMA) and romantic relationship satisfaction (RS ...
Is Social Media Addictive? Here's What the Science Says
Too many young consumers "can't put it down," he said. "The internet is a giant hypodermic, and the content, including social media like Meta, are the psychoactive drugs.". Matt Richtel ...

Social media addiction: Its impact, mediation, and intervention

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Peking University, China

Southwest University, China, Peking University, China

Tonglin Jiang

Cornell University, The USA

Introduction

Social Media Addiction and the Negative Outcomes

The Role of Self-Esteem

The Present Study

Results and Discussion

General discussion

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A review of theories and models applied in studies of social media addiction and implications for future research

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Investigation of the Effect of Social Media Addiction on Adults with Depression

Orhan Koçak

Thomas A. Shaw

Esra Zeynep Akpinar

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1. Introduction

2. Purpose of the Study

3.2. Data Collection Tools

3.3. Socio-Demographic Information Form

3.4. Social Media Addiction Scale (SMAS)

3.5. Beck Depression Inventory

3.6. Data Collection and Analysis

4.1. Socio-Demographic Features of Participants

4.2. Information on Participants’ Use of Social Media

4.3. Investigation of the Effect of Social Media Addiction on Depression

4.4. ANOVA Test Results with Multiple Variables

5. Discussion

6. Conclusions

Author Contributions

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Informed Consent Statement

Data Availability Statement

Exploring the Association Between Social Media Addiction and Relationship Satisfaction: Psychological Distress as a Mediator

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A two-study validation of a single-item measure of relationship satisfaction: RAS-1

Effects of attachment styles, dark triad, rejection sensitivity, and relationship satisfaction on social media addiction: A mediated model

Theoretical Framework

Social Media Addiction and Psychological Distress

Psychological Distress and Relationship Satisfaction

The Present Study

Participants and Procedure

Relationship Assessment Scale (RAS)

Social Media Disorder Scale (SMD)

Depression Anxiety and Stress Scale (DASS-21)

Statistical Analyses

Descriptive Statistics

Statistical Assumption Tests

Mediation Analyses

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Depression, Anxiety and Stress Scale: Cut-Off Criteria and Distribution of Participants

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