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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.

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.

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.

Crossref Cited-by

SYSTEMATIC REVIEW article

Research trends in social media addiction and problematic social media use: a bibliometric analysis.

• 1 Sasin School of Management, Chulalongkorn University, Bangkok, Thailand
• 2 Business Administration Division, Mahidol University International College, Mahidol University, Nakhon Pathom, Thailand

Despite their increasing ubiquity in people's lives and incredible advantages in instantly interacting with others, social media's impact on subjective well-being is a source of concern worldwide and calls for up-to-date investigations of the role social media plays in mental health. Much research has discovered how habitual social media use may lead to addiction and negatively affect adolescents' school performance, social behavior, and interpersonal relationships. The present study was conducted to review the extant literature in the domain of social media and analyze global research productivity during 2013–2022. Bibliometric analysis was conducted on 501 articles that were extracted from the Scopus database using the keywords social media addiction and problematic social media use. The data were then uploaded to VOSviewer software to analyze citations, co-citations, and keyword co-occurrences. Volume, growth trajectory, geographic distribution of the literature, influential authors, intellectual structure of the literature, and the most prolific publishing sources were analyzed. The bibliometric analysis presented in this paper shows that the US, the UK, and Turkey accounted for 47% of the publications in this field. Most of the studies used quantitative methods in analyzing data and therefore aimed at testing relationships between variables. In addition, the findings in this study show that most analysis were cross-sectional. Studies were performed on undergraduate students between the ages of 19–25 on the use of two social media platforms: Facebook and Instagram. Limitations as well as research directions for future studies are also discussed.

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 ( 1 ). Social networking sites such as Facebook, Instagram, and TikTok are prominent examples of social media that allow people to stay connected in an online world regardless of geographical distance or other obstacles ( 2 , 3 ). Recent evidence suggests that social networking sites have become increasingly popular among adolescents following the strict policies implemented by many countries to counter the COVID-19 pandemic, including social distancing, “lockdowns,” and quarantine measures ( 4 ). In this new context, social media have become an essential part of everyday life, especially for children and adolescents ( 5 ). For them such media are a means of socialization that connect people together. Interestingly, social media are not only used for social communication and entertainment purposes but also for sharing opinions, learning new things, building business networks, and initiate collaborative projects ( 6 ).

Among the 7.91 billion people in the world as of 2022, 4.62 billion active social media users, and the average time individuals spent using the internet was 6 h 58 min per day with an average use of social media platforms of 2 h and 27 min ( 7 ). Despite their increasing ubiquity in people's lives and the incredible advantages they offer to instantly interact with people, an increasing number of studies have linked social media use to negative mental health consequences, such as suicidality, loneliness, and anxiety ( 8 ). Numerous sources have expressed widespread concern about the effects of social media on mental health. A 2011 report by the American Academy of Pediatrics (AAP) identifies a phenomenon known as Facebook depression which may be triggered “when preteens and teens spend a great deal of time on social media sites, such as Facebook, and then begin to exhibit classic symptoms of depression” ( 9 ). Similarly, the UK's Royal Society for Public Health (RSPH) claims that there is a clear evidence of the relationship between social media use and mental health issues based on a survey of nearly 1,500 people between the ages of 14–24 ( 10 ). According to some authors, the increase in usage frequency of social media significantly increases the risks of clinical disorders described (and diagnosed) as “Facebook depression,” “fear of missing out” (FOMO), and “social comparison orientation” (SCO) ( 11 ). Other risks include sexting ( 12 ), social media stalking ( 13 ), cyber-bullying ( 14 ), privacy breaches ( 15 ), and improper use of technology. Therefore, social media's impact on subjective well-being is a source of concern worldwide and calls for up-to-date investigations of the role social media plays with regard to mental health ( 8 ). Many studies have found that habitual social media use may lead to addiction and thus negatively affect adolescents' school performance, social behavior, and interpersonal relationships ( 16 – 18 ). As a result of addiction, the user becomes highly engaged with online activities motivated by an uncontrollable desire to browse through social media pages and “devoting so much time and effort to it that it impairs other important life areas” ( 19 ).

Given these considerations, the present study was conducted to review the extant literature in the domain of social media and analyze global research productivity during 2013–2022. The study presents a bibliometric overview of the leading trends with particular regard to “social media addiction” and “problematic social media use.” This is valuable as it allows for a comprehensive overview of the current state of this field of research, as well as identifies any patterns or trends that may be present. Additionally, it provides information on the geographical distribution and prolific authors in this area, which may help to inform future research endeavors.

In terms of bibliometric analysis of social media addiction research, few studies have attempted to review the existing literature in the domain extensively. Most previous bibliometric studies on social media addiction and problematic use have focused mainly on one type of screen time activity such as digital gaming or texting ( 20 ) and have been conducted with a focus on a single platform such as Facebook, Instagram, or Snapchat ( 21 , 22 ). The present study adopts a more comprehensive approach by including all social media platforms and all types of screen time activities in its analysis.

Additionally, this review aims to highlight the major themes around which the research has evolved to date and draws some guidance for future research directions. In order to meet these objectives, this work is oriented toward answering the following research questions:

(1) What is the current status of research focusing on social media addiction?

(2) What are the key thematic areas in social media addiction and problematic use research?

(3) What is the intellectual structure of social media addiction as represented in the academic literature?

(4) What are the key findings of social media addiction and problematic social media research?

(5) What possible future research gaps can be identified in the field of social media addiction?

These research questions will be answered using bibliometric analysis of the literature on social media addiction and problematic use. This will allow for an overview of the research that has been conducted in this area, including information on the most influential authors, journals, countries of publication, and subject areas of study. Part 2 of the study will provide an examination of the intellectual structure of the extant literature in social media addiction while Part 3 will discuss the research methodology of the paper. Part 4 will discuss the findings of the study followed by a discussion under Part 5 of the paper. Finally, in Part 7, gaps in current knowledge about this field of research will be identified.

Literature review

Social media addiction research context.

Previous studies on behavioral addictions have looked at a lot of different factors that affect social media addiction focusing on personality traits. Although there is some inconsistency in the literature, numerous studies have focused on three main personality traits that may be associated with social media addiction, namely anxiety, depression, and extraversion ( 23 , 24 ).

It has been found that extraversion scores are strongly associated with increased use of social media and addiction to it ( 25 , 26 ). People with social anxiety as well as people who have psychiatric disorders often find online interactions extremely appealing ( 27 ). The available literature also reveals that the use of social media is positively associated with being female, single, and having attention deficit hyperactivity disorder (ADHD), obsessive compulsive disorder (OCD), or anxiety ( 28 ).

In a study by Seidman ( 29 ), the Big Five personality traits were assessed using Saucier's ( 30 ) Mini-Markers Scale. Results indicated that neurotic individuals use social media as a safe place for expressing their personality and meet belongingness needs. People affected by neurosis tend to use online social media to stay in touch with other people and feel better about their social lives ( 31 ). Narcissism is another factor that has been examined extensively when it comes to social media, and it has been found that people who are narcissistic are more likely to become addicted to social media ( 32 ). In this case users want to be seen and get “likes” from lots of other users. Longstreet and Brooks ( 33 ) did a study on how life satisfaction depends on how much money people make. Life satisfaction was found to be negatively linked to social media addiction, according to the results. When social media addiction decreases, the level of life satisfaction rises. But results show that in lieu of true-life satisfaction people use social media as a substitute (for temporary pleasure vs. longer term happiness).

Researchers have discovered similar patterns in students who tend to rank high in shyness: they find it easier to express themselves online rather than in person ( 34 , 35 ). With the use of social media, shy individuals have the opportunity to foster better quality relationships since many of their anxiety-related concerns (e.g., social avoidance and fear of social devaluation) are significantly reduced ( 36 , 37 ).

Problematic use of social media

The amount of research on problematic use of social media has dramatically increased since the last decade. But using social media in an unhealthy manner may not be considered an addiction or a disorder as this behavior has not yet been formally categorized as such ( 38 ). Although research has shown that people who use social media in a negative way often report negative health-related conditions, most of the data that have led to such results and conclusions comprise self-reported data ( 39 ). The dimensions of excessive social media usage are not exactly known because there are not enough diagnostic criteria and not enough high-quality long-term studies available yet. This is what Zendle and Bowden-Jones ( 40 ) noted in their own research. And this is why terms like “problematic social media use” have been used to describe people who use social media in a negative way. Furthermore, if a lot of time is spent on social media, it can be hard to figure out just when it is being used in a harmful way. For instance, people easily compare their appearance to what they see on social media, and this might lead to low self-esteem if they feel they do not look as good as the people they are following. According to research in this domain, the extent to which an individual engages in photo-related activities (e.g., taking selfies, editing photos, checking other people's photos) on social media is associated with negative body image concerns. Through curated online images of peers, adolescents face challenges to their self-esteem and sense of self-worth and are increasingly isolated from face-to-face interaction.

To address this problem the Diagnostic and Statistical Manual of Mental Disorders (DSM-V) has been used by some scholars ( 41 , 42 ). These scholars have used criteria from the DSM-V to describe one problematic social media use, internet gaming disorder, but such criteria could also be used to describe other types of social media disorders. Franchina et al. ( 43 ) and Scott and Woods ( 44 ), for example, focus their attention on individual-level factors (like fear of missing out) and family-level factors (like childhood abuse) that have been used to explain why people use social media in a harmful way. Friends-level factors have also been explored as a social well-being measurement to explain why people use social media in a malevolent way and demonstrated significant positive correlations with lower levels of friend support ( 45 ). Macro-level factors have also been suggested, such as the normalization of surveillance ( 46 ) and the ability to see what people are doing online ( 47 ). Gender and age seem to be highly associated to the ways people use social media negatively. Particularly among girls, social media use is consistently associated with mental health issues ( 41 , 48 , 49 ), an association more common among older girls than younger girls ( 46 , 48 ).

Most studies have looked at the connection between social media use and its effects (such as social media addiction) and a number of different psychosomatic disorders. In a recent study conducted by Vannucci and Ohannessian ( 50 ), the use of social media appears to have a variety of effects “on psychosocial adjustment during early adolescence, with high social media use being the most problematic.” It has been found that people who use social media in a harmful way are more likely to be depressed, anxious, have low self-esteem, be more socially isolated, have poorer sleep quality, and have more body image dissatisfaction. Furthermore, harmful social media use has been associated with unhealthy lifestyle patterns (for example, not getting enough exercise or having trouble managing daily obligations) as well as life threatening behaviors such as illicit drug use, excessive alcohol consumption and unsafe sexual practices ( 51 , 52 ).

A growing body of research investigating social media use has revealed that the extensive use of social media platforms is correlated with a reduced performance on cognitive tasks and in mental effort ( 53 ). Overall, it appears that individuals who have a problematic relationship with social media or those who use social media more frequently are more likely to develop negative health conditions.

Social media addiction and problematic use systematic reviews

Previous studies have revealed the detrimental impacts of social media addiction on users' health. A systematic review by Khan and Khan ( 20 ) has pointed out that social media addiction has a negative impact on users' mental health. For example, social media addiction can lead to stress levels rise, loneliness, and sadness ( 54 ). Anxiety is another common mental health problem associated with social media addiction. Studies have found that young adolescents who are addicted to social media are more likely to suffer from anxiety than people who are not addicted to social media ( 55 ). In addition, social media addiction can also lead to physical health problems, such as obesity and carpal tunnel syndrome a result of spending too much time on the computer ( 22 ).

Apart from the negative impacts of social media addiction on users' mental and physical health, social media addiction can also lead to other problems. For example, social media addiction can lead to financial problems. A study by Sharif and Yeoh ( 56 ) has found that people who are addicted to social media tend to spend more money than those who are not addicted to social media. In addition, social media addiction can also lead to a decline in academic performance. Students who are addicted to social media are more likely to have lower grades than those who are not addicted to social media ( 57 ).

Research methodology

Bibliometric analysis.

Merigo et al. ( 58 ) use bibliometric analysis to examine, organize, and analyze a large body of literature from a quantitative, objective perspective in order to assess patterns of research and emerging trends in a certain field. A bibliometric methodology is used to identify the current state of the academic literature, advance research. and find objective information ( 59 ). This technique allows the researchers to examine previous scientific work, comprehend advancements in prior knowledge, and identify future study opportunities.

To achieve this objective and identify the research trends in social media addiction and problematic social media use, this study employs two bibliometric methodologies: performance analysis and science mapping. Performance analysis uses a series of bibliometric indicators (e.g., number of annual publications, document type, source type, journal impact factor, languages, subject area, h-index, and countries) and aims at evaluating groups of scientific actors on a particular topic of research. VOSviewer software ( 60 ) was used to carry out the science mapping. The software is used to visualize a particular body of literature and map the bibliographic material using the co-occurrence analysis of author, index keywords, nations, and fields of publication ( 61 , 62 ).

Data collection

After picking keywords, designing the search strings, and building up a database, the authors conducted a bibliometric literature search. Scopus was utilized to gather exploration data since it is a widely used database that contains the most comprehensive view of the world's research output and provides one of the most effective search engines. If the research was to be performed using other database such as Web Of Science or Google Scholar the authors may have obtained larger number of articles however they may not have been all particularly relevant as Scopus is known to have the most widest and most relevant scholar search engine in marketing and social science. A keyword search for “social media addiction” OR “problematic social media use” yielded 553 papers, which were downloaded from Scopus. The information was gathered in March 2022, and because the Scopus database is updated on a regular basis, the results may change in the future. Next, the authors examined the titles and abstracts to see whether they were relevant to the topics treated. There were two common grounds for document exclusion. First, while several documents emphasized the negative effects of addiction in relation to the internet and digital media, they did not focus on social networking sites specifically. Similarly, addiction and problematic consumption habits were discussed in relation to social media in several studies, although only in broad terms. This left a total of 511 documents. Articles were then limited only to journal articles, conference papers, reviews, books, and only those published in English. This process excluded 10 additional documents. Then, the relevance of the remaining articles was finally checked by reading the titles, abstracts, and keywords. Documents were excluded if social networking sites were only mentioned as a background topic or very generally. This resulted in a final selection of 501 research papers, which were then subjected to bibliometric analysis (see Figure 1 ).

Figure 1 . Preferred reporting items for systematic reviews and meta-analysis (PRISMA) flowchart showing the search procedures used in the review.

After identifying 501 Scopus files, bibliographic data related to these documents were imported into an Excel sheet where the authors' names, their affiliations, document titles, keywords, abstracts, and citation figures were analyzed. These were subsequently uploaded into VOSViewer software version 1.6.8 to begin the bibliometric review. Descriptive statistics were created to define the whole body of knowledge about social media addiction and problematic social media use. VOSViewer was used to analyze citation, co-citation, and keyword co-occurrences. According to Zupic and Cater ( 63 ), co-citation analysis measures the influence of documents, authors, and journals heavily cited and thus considered influential. Co-citation analysis has the objective of building similarities between authors, journals, and documents and is generally defined as the frequency with which two units are cited together within the reference list of a third article.

The implementation of social media addiction performance analysis was conducted according to the models recently introduced by Karjalainen et al. ( 64 ) and Pattnaik ( 65 ). Throughout the manuscript there are operational definitions of relevant terms and indicators following a standardized bibliometric approach. The cumulative academic impact (CAI) of the documents was measured by the number of times they have been cited in other scholarly works while the fine-grained academic impact (FIA) was computed according to the authors citation analysis and authors co-citation analysis within the reference lists of documents that have been specifically focused on social media addiction and problematic social media use.

Results of the study presented here include the findings on social media addiction and social media problematic use. The results are presented by the foci outlined in the study questions.

Volume, growth trajectory, and geographic distribution of the literature

After performing the Scopus-based investigation of the current literature regarding social media addiction and problematic use of social media, the authors obtained a knowledge base consisting of 501 documents comprising 455 journal articles, 27 conference papers, 15 articles reviews, 3 books and 1 conference review. The included literature was very recent. As shown in Figure 2 , publication rates started very slowly in 2013 but really took off in 2018, after which publications dramatically increased each year until a peak was reached in 2021 with 195 publications. Analyzing the literature published during the past decade reveals an exponential increase in scholarly production on social addiction and its problematic use. This might be due to the increasingly widespread introduction of social media sites in everyday life and the ubiquitous diffusion of mobile devices that have fundamentally impacted human behavior. The dip in the number of publications in 2022 is explained by the fact that by the time the review was carried out the year was not finished yet and therefore there are many articles still in press.

Figure 2 . Annual volume of social media addiction or social media problematic use ( n = 501).

The geographical distribution trends of scholarly publications on social media addiction or problematic use of social media are highlighted in Figure 3 . The articles were assigned to a certain country according to the nationality of the university with whom the first author was affiliated with. The figure shows that the most productive countries are the USA (92), the U.K. (79), and Turkey ( 63 ), which combined produced 236 articles, equal to 47% of the entire scholarly production examined in this bibliometric analysis. Turkey has slowly evolved in various ways with the growth of the internet and social media. Anglo-American scholarly publications on problematic social media consumer behavior represent the largest research output. Yet it is interesting to observe that social networking sites studies are attracting many researchers in Asian countries, particularly China. For many Chinese people, social networking sites are a valuable opportunity to involve people in political activism in addition to simply making purchases ( 66 ).

Figure 3 . Global dispersion of social networking sites in relation to social media addiction or social media problematic use.

Analysis of influential authors

This section analyses the high-impact authors in the Scopus-indexed knowledge base on social networking sites in relation to social media addiction or problematic use of social media. It provides valuable insights for establishing patterns of knowledge generation and dissemination of literature about social networking sites relating to addiction and problematic use.

Table 1 acknowledges the top 10 most highly cited authors with the highest total citations in the database.

Table 1 . Highly cited authors on social media addiction and problematic use ( n = 501).

Table 1 shows that MD Griffiths (sixty-five articles), CY Lin (twenty articles), and AH Pakpour (eighteen articles) are the most productive scholars according to the number of Scopus documents examined in the area of social media addiction and its problematic use . If the criteria are changed and authors ranked according to the overall number of citations received in order to determine high-impact authors, the same three authors turn out to be the most highly cited authors. It should be noted that these highly cited authors tend to enlist several disciplines in examining social media addiction and problematic use. Griffiths, for example, focuses on behavioral addiction stemming from not only digital media usage but also from gambling and video games. Lin, on the other hand, focuses on the negative effects that the internet and digital media can have on users' mental health, and Pakpour approaches the issue from a behavioral medicine perspective.

Intellectual structure of the literature

In this part of the paper, the authors illustrate the “intellectual structure” of the social media addiction and the problematic use of social media's literature. An author co-citation analysis (ACA) was performed which is displayed as a figure that depicts the relations between highly co-cited authors. The study of co-citation assumes that strongly co-cited authors carry some form of intellectual similarity ( 67 ). Figure 4 shows the author co-citation map. Nodes represent units of analysis (in this case scholars) and network ties represent similarity connections. Nodes are sized according to the number of co-citations received—the bigger the node, the more co-citations it has. Adjacent nodes are considered intellectually similar.

Figure 4 . Two clusters, representing the intellectual structure of the social media and its problematic use literature.

Scholars belonging to the green cluster (Mental Health and Digital Media Addiction) have extensively published on medical analysis tools and how these can be used to heal users suffering from addiction to digital media, which can range from gambling, to internet, to videogame addictions. Scholars in this school of thought focus on the negative effects on users' mental health, such as depression, anxiety, and personality disturbances. Such studies focus also on the role of screen use in the development of mental health problems and the increasing use of medical treatments to address addiction to digital media. They argue that addiction to digital media should be considered a mental health disorder and treatment options should be made available to users.

In contrast, scholars within the red cluster (Social Media Effects on Well Being and Cyberpsychology) have focused their attention on the effects of social media toward users' well-being and how social media change users' behavior, focusing particular attention on the human-machine interaction and how methods and models can help protect users' well-being. Two hundred and two authors belong to this group, the top co-cited being Andreassen (667 co-citations), Pallasen (555 co-citations), and Valkenburg (215 co-citations). These authors have extensively studied the development of addiction to social media, problem gambling, and internet addiction. They have also focused on the measurement of addiction to social media, cyberbullying, and the dark side of social media.

Most influential source title in the field of social media addiction and its problematic use

To find the preferred periodicals in the field of social media addiction and its problematic use, the authors have selected 501 articles published in 263 journals. Table 2 gives a ranked list of the top 10 journals that constitute the core publishing sources in the field of social media addiction research. In doing so, the authors analyzed the journal's impact factor, Scopus Cite Score, h-index, quartile ranking, and number of publications per year.

Table 2 . Top 10 most cited and more frequently mentioned documents in the field of social media addiction.

The journal Addictive Behaviors topped the list, with 700 citations and 22 publications (4.3%), followed by Computers in Human Behaviors , with 577 citations and 13 publications (2.5%), Journal of Behavioral Addictions , with 562 citations and 17 publications (3.3%), and International Journal of Mental Health and Addiction , with 502 citations and 26 publications (5.1%). Five of the 10 most productive journals in the field of social media addiction research are published by Elsevier (all Q1 rankings) while Springer and Frontiers Media published one journal each.

Documents citation analysis identified the most influential and most frequently mentioned documents in a certain scientific field. Andreassen has received the most citations among the 10 most significant papers on social media addiction, with 405 ( Table 2 ). The main objective of this type of studies was to identify the associations and the roles of different variables as predictors of social media addiction (e.g., ( 19 , 68 , 69 )). According to general addiction models, the excessive and problematic use of digital technologies is described as “being overly concerned about social media, driven by an uncontrollable motivation to log on to or use social media, and devoting so much time and effort to social media that it impairs other important life areas” ( 27 , 70 ). Furthermore, the purpose of several highly cited studies ( 31 , 71 ) was to analyse the connections between young adults' sleep quality and psychological discomfort, depression, self-esteem, and life satisfaction and the severity of internet and problematic social media use, since the health of younger generations and teenagers is of great interest this may help explain the popularity of such papers. Despite being the most recent publication Lin et al.'s work garnered more citations annually. The desire to quantify social media addiction in individuals can also help explain the popularity of studies which try to develop measurement scales ( 42 , 72 ). Some of the highest-ranked publications are devoted to either the presentation of case studies or testing relationships among psychological constructs ( 73 ).

Keyword co-occurrence analysis

The research question, “What are the key thematic areas in social media addiction literature?” was answered using keyword co-occurrence analysis. Keyword co-occurrence analysis is conducted to identify research themes and discover keywords. It mainly examines the relationships between co-occurrence keywords in a wide variety of literature ( 74 ). In this approach, the idea is to explore the frequency of specific keywords being mentioned together.

Utilizing VOSviewer, the authors conducted a keyword co-occurrence analysis to characterize and review the developing trends in the field of social media addiction. The top 10 most frequent keywords are presented in Table 3 . The results indicate that “social media addiction” is the most frequent keyword (178 occurrences), followed by “problematic social media use” (74 occurrences), “internet addiction” (51 occurrences), and “depression” (46 occurrences). As shown in the co-occurrence network ( Figure 5 ), the keywords can be grouped into two major clusters. “Problematic social media use” can be identified as the core theme of the green cluster. In the red cluster, keywords mainly identify a specific aspect of problematic social media use: social media addiction.

Table 3 . Frequency of occurrence of top 10 keywords.

Figure 5 . Keywords co-occurrence map. Threshold: 5 co-occurrences.

The results of the keyword co-occurrence analysis for journal articles provide valuable perspectives and tools for understanding concepts discussed in past studies of social media usage ( 75 ). More precisely, it can be noted that there has been a large body of research on social media addiction together with other types of technological addictions, such as compulsive web surfing, internet gaming disorder, video game addiction and compulsive online shopping ( 76 – 78 ). This field of research has mainly been directed toward teenagers, middle school students, and college students and university students in order to understand the relationship between social media addiction and mental health issues such as depression, disruptions in self-perceptions, impairment of social and emotional activity, anxiety, neuroticism, and stress ( 79 – 81 ).

The findings presented in this paper show that there has been an exponential increase in scholarly publications—from two publications in 2013 to 195 publications in 2021. There were 45 publications in 2022 at the time this study was conducted. It was interesting to observe that the US, the UK, and Turkey accounted for 47% of the publications in this field even though none of these countries are in the top 15 countries in terms of active social media penetration ( 82 ) although the US has the third highest number of social media users ( 83 ). Even though China and India have the highest number of social media users ( 83 ), first and second respectively, they rank fifth and tenth in terms of publications on social media addiction or problematic use of social media. In fact, the US has almost double the number of publications in this field compared to China and almost five times compared to India. Even though East Asia, Southeast Asia, and South Asia make up the top three regions in terms of worldwide social media users ( 84 ), except for China and India there have been only a limited number of publications on social media addiction or problematic use. An explanation for that could be that there is still a lack of awareness on the negative consequences of the use of social media and the impact it has on the mental well-being of users. More research in these regions should perhaps be conducted in order to understand the problematic use and addiction of social media so preventive measures can be undertaken.

From the bibliometric analysis, it was found that most of the studies examined used quantitative methods in analyzing data and therefore aimed at testing relationships between variables. In addition, many studies were empirical, aimed at testing relationships based on direct or indirect observations of social media use. Very few studies used theories and for the most part if they did they used the technology acceptance model and social comparison theories. The findings presented in this paper show that none of the studies attempted to create or test new theories in this field, perhaps due to the lack of maturity of the literature. Moreover, neither have very many qualitative studies been conducted in this field. More qualitative research in this field should perhaps be conducted as it could explore the motivations and rationales from which certain users' behavior may arise.

The authors found that almost all the publications on social media addiction or problematic use relied on samples of undergraduate students between the ages of 19–25. The average daily time spent by users worldwide on social media applications was highest for users between the ages of 40–44, at 59.85 min per day, followed by those between the ages of 35–39, at 59.28 min per day, and those between the ages of 45–49, at 59.23 per day ( 85 ). Therefore, more studies should be conducted exploring different age groups, as users between the ages of 19–25 do not represent the entire population of social media users. Conducting studies on different age groups may yield interesting and valuable insights to the field of social media addiction. For example, it would be interesting to measure the impacts of social media use among older users aged 50 years or older who spend almost the same amount of time on social media as other groups of users (56.43 min per day) ( 85 ).

A majority of the studies tested social media addiction or problematic use based on only two social media platforms: Facebook and Instagram. Although Facebook and Instagram are ranked first and fourth in terms of most popular social networks by number of monthly users, it would be interesting to study other platforms such as YouTube, which is ranked second, and WhatsApp, which is ranked third ( 86 ). Furthermore, TikTok would also be an interesting platform to study as it has grown in popularity in recent years, evident from it being the most downloaded application in 2021, with 656 million downloads ( 87 ), and is ranked second in Q1 of 2022 ( 88 ). Moreover, most of the studies focused only on one social media platform. Comparing different social media platforms would yield interesting results because each platform is different in terms of features, algorithms, as well as recommendation engines. The purpose as well as the user behavior for using each platform is also different, therefore why users are addicted to these platforms could provide a meaningful insight into social media addiction and problematic social media use.

Lastly, most studies were cross-sectional, and not longitudinal, aiming at describing results over a certain point in time and not over a long period of time. A longitudinal study could better describe the long-term effects of social media use.

This study was conducted to review the extant literature in the field of social media and analyze the global research productivity during the period ranging from 2013 to 2022. The study presents a bibliometric overview of the leading trends with particular regard to “social media addiction” and “problematic social media use.” The authors applied science mapping to lay out a knowledge base on social media addiction and its problematic use. This represents the first large-scale analysis in this area of study.

A keyword search of “social media addiction” OR “problematic social media use” yielded 553 papers, which were downloaded from Scopus. After performing the Scopus-based investigation of the current literature regarding social media addiction and problematic use, the authors ended up with a knowledge base consisting of 501 documents comprising 455 journal articles, 27 conference papers, 15 articles reviews, 3 books, and 1 conference review.

The geographical distribution trends of scholarly publications on social media addiction or problematic use indicate that the most productive countries were the USA (92), the U.K. (79), and Turkey ( 63 ), which together produced 236 articles. Griffiths (sixty-five articles), Lin (twenty articles), and Pakpour (eighteen articles) were the most productive scholars according to the number of Scopus documents examined in the area of social media addiction and its problematic use. An author co-citation analysis (ACA) was conducted which generated a layout of social media effects on well-being and cyber psychology as well as mental health and digital media addiction in the form of two research literature clusters representing the intellectual structure of social media and its problematic use.

The preferred periodicals in the field of social media addiction and its problematic use were Addictive Behaviors , with 700 citations and 22 publications, followed by Computers in Human Behavior , with 577 citations and 13 publications, and Journal of Behavioral Addictions , with 562 citations and 17 publications. Keyword co-occurrence analysis was used to investigate the key thematic areas in the social media literature, as represented by the top three keyword phrases in terms of their frequency of occurrence, namely, “social media addiction,” “problematic social media use,” and “social media addiction.”

This research has a few limitations. The authors used science mapping to improve the comprehension of the literature base in this review. First and foremost, the authors want to emphasize that science mapping should not be utilized in place of established review procedures, but rather as a supplement. As a result, this review can be considered the initial stage, followed by substantive research syntheses that examine findings from recent research. Another constraint stems from how 'social media addiction' is defined. The authors overcame this limitation by inserting the phrase “social media addiction” OR “problematic social media use” in the search string. The exclusive focus on SCOPUS-indexed papers creates a third constraint. The SCOPUS database has a larger number of papers than does Web of Science although it does not contain all the publications in a given field.

Although the total body of literature on social media addiction is larger than what is covered in this review, the use of co-citation analyses helped to mitigate this limitation. This form of bibliometric study looks at all the publications listed in the reference list of the extracted SCOPUS database documents. As a result, a far larger dataset than the one extracted from SCOPUS initially has been analyzed.

The interpretation of co-citation maps should be mentioned as a last constraint. The reason is that the procedure is not always clear, so scholars must have a thorough comprehension of the knowledge base in order to make sense of the result of the analysis ( 63 ). This issue was addressed by the authors' expertise, but it remains somewhat subjective.

Implications

The findings of this study have implications mainly for government entities and parents. The need for regulation of social media addiction is evident when considering the various risks associated with habitual social media use. Social media addiction may lead to negative consequences for adolescents' school performance, social behavior, and interpersonal relationships. In addition, social media addiction may also lead to other risks such as sexting, social media stalking, cyber-bullying, privacy breaches, and improper use of technology. Given the seriousness of these risks, it is important to have regulations in place to protect adolescents from the harms of social media addiction.

Regulation of social media platforms

One way that regulation could help protect adolescents from the harms of social media addiction is by limiting their access to certain websites or platforms. For example, governments could restrict adolescents' access to certain websites or platforms during specific hours of the day. This would help ensure that they are not spending too much time on social media and are instead focusing on their schoolwork or other important activities.

Another way that regulation could help protect adolescents from the harms of social media addiction is by requiring companies to put warning labels on their websites or apps. These labels would warn adolescents about the potential risks associated with excessive use of social media.

Finally, regulation could also require companies to provide information about how much time each day is recommended for using their website or app. This would help adolescents make informed decisions about how much time they want to spend on social media each day. These proposed regulations would help to protect children from the dangers of social media, while also ensuring that social media companies are more transparent and accountable to their users.

Parental involvement in adolescents' social media use

Parents should be involved in their children's social media use to ensure that they are using these platforms safely and responsibly. Parents can monitor their children's online activity, set time limits for social media use, and talk to their children about the risks associated with social media addiction.

Education on responsible social media use

Adolescents need to be educated about responsible social media use so that they can enjoy the benefits of these platforms while avoiding the risks associated with addiction. Education on responsible social media use could include topics such as cyber-bullying, sexting, and privacy breaches.

Research directions for future studies

A content analysis was conducted to answer the fifth research questions “What are the potential research directions for addressing social media addiction in the future?” The study reveals that there is a lack of screening instruments and diagnostic criteria to assess social media addiction. Validated DSM-V-based instruments could shed light on the factors behind social media use disorder. Diagnostic research may be useful in order to understand social media behavioral addiction and gain deeper insights into the factors responsible for psychological stress and psychiatric disorders. In addition to cross-sectional studies, researchers should also conduct longitudinal studies and experiments to assess changes in users' behavior over time ( 20 ).

Another important area to examine is the role of engagement-based ranking and recommendation algorithms in online habit formation. More research is required to ascertain how algorithms determine which content type generates higher user engagement. A clear understanding of the way social media platforms gather content from users and amplify their preferences would lead to the development of a standardized conceptualization of social media usage patterns ( 89 ). This may provide a clearer picture of the factors that lead to problematic social media use and addiction. It has been noted that “misinformation, toxicity, and violent content are inordinately prevalent” in material reshared by users and promoted by social media algorithms ( 90 ).

Additionally, an understanding of engagement-based ranking models and recommendation algorithms is essential in order to implement appropriate public policy measures. To address the specific behavioral concerns created by social media, legislatures must craft appropriate statutes. Thus, future qualitative research to assess engagement based ranking frameworks is extremely necessary in order to provide a broader perspective on social media use and tackle key regulatory gaps. Particular emphasis must be placed on consumer awareness, algorithm bias, privacy issues, ethical platform design, and extraction and monetization of personal data ( 91 ).

From a geographical perspective, the authors have identified some main gaps in the existing knowledge base that uncover the need for further research in certain regions of the world. Accordingly, the authors suggest encouraging more studies on internet and social media addiction in underrepresented regions with high social media penetration rates such as Southeast Asia and South America. In order to draw more contributions from these countries, journals with high impact factors could also make specific calls. This would contribute to educating social media users about platform usage and implement policy changes that support the development of healthy social media practices.

The authors hope that the findings gathered here will serve to fuel interest in this topic and encourage other scholars to investigate social media addiction in other contexts on newer platforms and among wide ranges of sample populations. In light of the rising numbers of people experiencing mental health problems (e.g., depression, anxiety, food disorders, and substance addiction) in recent years, it is likely that the number of papers related to social media addiction and the range of countries covered will rise even further.

Data availability statement

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

Author contributions

AP took care of bibliometric analysis and drafting the paper. VB took care of proofreading and adding value to the paper. AS took care of the interpretation of the findings. All authors contributed to the article and approved the submitted version.

Conflict of interest

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

Publisher's note

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

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Keywords: bibliometric analysis, social media, social media addiction, problematic social media use, research trends

Citation: Pellegrino A, Stasi A and Bhatiasevi V (2022) Research trends in social media addiction and problematic social media use: A bibliometric analysis. Front. Psychiatry 13:1017506. doi: 10.3389/fpsyt.2022.1017506

Received: 12 August 2022; Accepted: 24 October 2022; Published: 10 November 2022.

Reviewed by:

Copyright © 2022 Pellegrino, Stasi and Bhatiasevi. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Alfonso Pellegrino, alfonso.pellegrino@sasin.edu ; Veera Bhatiasevi, veera.bhatiasevi@mahidol.ac.th

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

Social Media Addiction

• First Online: 25 June 2023

Cite this chapter

• Teresa Berenice Treviño Benavides   ORCID: orcid.org/0000-0003-4993-3701 6 ,
• Ana Teresa Alcorta Castro 6 ,
• Sofia Alejandra Garza Marichalar 6 ,
• Mariamiranda Peña Cisneros 6 &
• Elena Catalina Baker Suárez 6  

Part of the book series: SpringerBriefs in Business ((BRIEFSBUSINESS))

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This chapter begins discussing the pathological Internet use, as a basis to understand how such behavior is similar to other type of addiction such as gambling, as they both are impulse control disorders. Further, the cognitive-behavioral model of pathological internet use (PIU) is presented and explained throughout its components. Particularly, the chapter explains that maladaptive cognitions, or false beliefs, also called irrational beliefs may ultimately be linked to problematic Internet use. Additionally, this chapter addresses the possible components or factors that may lead users do develop social media addiction, such as sleep disorders, length of use, anxiety or depression, family discomfort, unfavorable work or academic situation, and family problems. Finally, some outcomes or consequences of social media addiction are explored, which include depression, insomnia, anxiety and negatively associated with life satisfaction, well-being, and affected academic performance. The chapter concludes by offering a summary of the most important concepts in the literature review associated with social media addiction.

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Treviño Benavides, T.B., Alcorta Castro, A.T., Garza Marichalar, S.A., Peña Cisneros, M., Baker Suárez, E.C. (2023). Social Media Addiction. In: Social Media Addiction in Generation Z Consumers. SpringerBriefs in Business. Springer, Cham. https://doi.org/10.1007/978-3-031-33452-8_3

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Social media use and abuse: Different profiles of users and their associations with addictive behaviours

Deon tullett-prado.

a Victoria University, Australia

Vasileios Stavropoulos

b University of Athens, Greece

Rapson Gomez

c Federation University, Australia

Associated Data

The data is made available via a link document.

Introduction

Social media use has become increasingly prevalent worldwide. Simultaneously, concerns surrounding social media abuse/problematic use, which resembles behavioural and substance addictions, have proliferated. This has prompted the introduction of ‘Social Media Addiction’ [SMA], as a condition requiring clarifications regarding its definition, assessment and associations with other addictions. Thus, this study aimed to: (a) advance knowledge on the typology/structure of SMA symptoms experienced and: (b) explore the association of these typologies with addictive behaviours related to gaming, gambling, alcohol, smoking, drug abuse, sex (including porn), shopping, internet use, and exercise.

A sample of 968 [Mage = 29.5, SDage = 9.36, nmales = 622 (64.3 %), nfemales = 315, (32.5 %)] adults was surveyed regarding their SMA experiences, using the Bergen Social Media Addiction Scale (BSMAS). Their experiences of Gaming, Internet, Gambling, Alcohol, Cigarette, Drug, Sex, Shopping and Exercise addictions were additionally assessed, and latent profile analysis (LPA) was implemented.

Three distinct profiles were revealed, based on the severity of one’s SMA symptoms: ‘low’, ‘moderate’ and ‘high’ risk. Subsequent ANOVA analyses suggested that participants classified as ‘high’ risk indicated significantly higher behaviours related to internet, gambling, gaming, sex and in particular shopping addictions.

Conclusions

Results support SMA as a unitary construct, while they potentially challenge the distinction between technological and behavioural addictions. Findings also imply that the assessment of those presenting with SMA behaviours, as well as prevention and intervention targeting SMA at risk groups, should consider other comorbid addictions.

1. Introduction

Social media – a form of online communication in which users create profiles, generate and share content, while forming online social networks/communities ( Obar & Wildman, 2015 ), is quickly growing to become almost all consuming in the media landscape. Currently the number of daily social media users exceeds 53 % (∼4.5 billion users) of the global population, approaching 80 % among more developed nations ( Countrymeters, 2021 , DataReportal, 2021 ). Due to technological advancements, the rise of ‘digital natives’ (i.e. children and adolescents raised with and familiarised with digital technology) and coronavirus pandemic triggered lockdowns, the frequency and duration of social media usage has been steadily increasing as people compensate for a lack of face to face interaction or grow with Social Media as a normal part of their lives (i.e. ∼ 2 h and 27 min average daily; DataReportal, 2021 , Heffer et al., 2019 , Zhong et al., 2020 , Nguyen, 2021 ). Furthermore, social media is increasingly involved in various domains of life including education, economics and even politics, to the point where engagement with the economy and wider society almost necessitates its use, driving the continued proliferation of social media use ( Calderaro, 2018 , Nguyen, 2021 , Mabić et al., 2020 , Mourão and Kilgo, 2021 ). This societal shift towards increased social media use has had some positive benefits, serving to facilitate the creation and maintenance of social groups, increase access to opportunities for career advancement and created wide ranging and accessible education options for many users ( Calderaro, 2018 , Prinstein et al., 2020 , Bouchillon, 2020 , Nguyen, 2021 ). However, for a minority of users - roughly 5–10 % ( Bányai et al., 2017 , Luo et al., 2021 , Brailovskaia et al., 2021 ) – social media use has become excessive, to the point where it dominates one’s life, similarly to an addictive behaviour - a state known as 'problematic social media use' ( Sun & Zhang, 2020 ). For these users, social media is experienced as the single most important activity in one’s life, while compromising their other roles and obligations (e.g. family, romance, employment; Sun and Zhang, 2020 , Griffiths and Kuss, 2017 ). This is a situation associated with low mood/depression, the compromise of one’s identity, social comparison leading to anxiety and self-esteem issues, work, academic/career difficulties, compromised sleep schedules and physical health, and even social impairment leading to isolation ( Anderson et al., 2017 , Sun and Zhang, 2020 , Gorwa and Guilbeault, 2020 ).

1.1. Problematic social media engagement in the context of addictions

Problematic social media use is markedly similar to the experience of substance addiction, thus leading to problematic social media use being modelled by some as a behavioural addiction - social media addiction (SMA; Sun and Zhang, 2020 ). In brief, an addiction loosely refers to a state where an individual experiences a powerful craving to engage with a behaviour, and inability to control their related actions, such that it begins to negatively impact their life ( Starcevic, 2016 ). Although initially the term referred to substance addictions induced by psychotropic drugs (e.g., amphetamines), it later expanded to include behavioural addictions ( Chamberlain et al., 2016 ). These reflect a fixation and lack of control, similar to those experienced in the abuse of substances, related to one’s excessive/problematic behaviours ( Starcevic, 2016 ).

Indeed, behavioural addictions, such as gaming, gambling and (arguably) social media addiction (SMA) share many common features with substance related addictions ( Zarate et al., 2022 ). Their similarities extend beyond the core addiction manifestations of fixation, loss of control and negative life consequences ( Grant et al., 2010 , Bodor et al., 2016 , Martinac et al., 2019 , Zarate et al., 2022 ). For instance, it has been evidenced that common risk factors/mechanisms (e.g., low impulse control), behavioural patterns (e.g., chronic relapse; sudden “spontaneous” quitting), ages of onset (e.g., adolescence and young adulthood) and negative life consequences (e.g., financial and legal difficulties) are similar between the so-called behavioural addictions and formally diagnosed substance addictions ( Grant et al., 2010 ). Moreover, such commonalities often accommodate the concurrent experience of addictive presentations, and/or even the substitution/flow from one addiction to the next (e.g., gambling and alcoholism; Bodor et al., 2016 , Martinac et al., 2019 , Grant et al., 2010 ).

With these features in mind, SMA has been depicted as characterized by the following six symptoms; A deep preoccupation with social media use (salience), use to either increase their positive feelings and/or buffer their negative feelings (mood modification), the requirement for progressively increasing time-engagement to get the same effect (i.e., tolerance), withdrawal symptoms such as irritability and frustration when access is reduced (withdrawal), the development of tensions with other people due to under-performance across several life domains (conflict) and reduced self-regulation resulting in an inability to reduce use (relapse; Andreassen et al., 2012 , Brown, 1993 , Griffiths and Kuss, 2017 , Sun and Zhang, 2020 ).

This developing model of SMA has been gaining popularity as the most widely used conceptualisation of problematic social media use, and guiding the development of relevant measurement tools ( Andreassen et al., 2012 , Haand and Shuwang, 2020 , Prinstein et al., 2020 ; Van den Eijnden et al., 2016) ). However, SMA is not currently uniformly accepted as an understanding of problematic social media use. Some critics have labelled the SMA model a premature pathologisation of ordinary social media use behaviours with low construct validity and little evidence for its existence, often inviting alternative proposed classifications derived by cognitive-behavioural or contextual models ( Sun & Zhang, 2020 ; Panova & Carbonell, 2018 7; Moretta, Buodo, Demetrovics & Potenza, 2022 ). Furthermore, the causes, risk factors and consequences of SMA, as well as the measures employed in its assessment have yet to be elucidated in depth, with research in the area being largely exploratory in nature ( Prinstein et al., 2020 , Sun and Zhang, 2020 ). In this context, what functional, regular and excessive social media use behaviours may involve has also been debated ( Wegmann et al., 2022 ). Thus, there is a need for further research clarifying the nature of SMA, identifying risk factors and related negative outcomes, as well as potential methods of treatment ( Prinstein et al., 2020 , Sun and Zhang, 2020 , Moretta et al., 2022 ).

Two avenues important for realizing these goals (and the focus of this study) involve: a) profiling SMA behaviours in the broader community, and b) decoding their associations with other addictions. Profiling these behaviours would involve identifying groups of people with particular patterns of use rather than simply examining trends in behaviour across the greater population. This would allow for clearer understandings of the ways in which different groups experience SMA and a more person-centred analysis (i.e., focused on finer understandings of personal experiences, Bányai et al., 2017 ). Moreover, when combined with analyses of association, it can allow for assertions not only about whether SMA associates with a variable, but about which components of the experience of SMA associate with a variable, allowing for more nuanced understandings. One such association with much potential for exploration, is that of SMA with other addictions (i.e., how does a certain SMA type differentially relate with other addictive behaviors, such as gambling and/or substance abuse?). Such knowledge would be useful, due to the shared common features and risk factors between addictions. It would allow for a greater understanding of the likelihood of comorbid addictions, or of flow from one addiction to the next ( Bodor et al., 2016 , Martinac et al., 2019 , Grant et al., 2010 ). However, the various links between different addictions are not identical, with alcoholism (for example) associating less strongly with excessive/problematic internet use than with problematic/excessive (so called “addictive) sex behaviours ( Grant et al., 2010 ). In that line, some studies have suggested the consideration of different addiction subgroups (e.g., substance, behavioural and technology addictions Marmet et al., 2019 ), and/or different profiles of individuals being prone to manifest some addictive behaviours more than others ( Zilberman et al., 2018 ). Accordingly, one may assume that distinct profiles of those suffering from SMA behaviours may be more at risk for certain addictions over others, rather than with addictions in general ( Zarate et al., 2022 ).

Understanding these varying connections could be vital for SMA treatment. Co-occurring addictions often reinforce each-other through their behavioural effects. Furthermore, by targeting only a single addiction type in a treatment, other addictions an individual is vulnerable to can come to the fore ( Grant et al., 2010 , Miller et al., 2019 ). Thus, a holistic view of addictive vulnerability may require consideration ( Grant et al., 2010 , Miller et al., 2019 ). This makes the identification of individual SMA profiles, as well as any potential co-occurring addictions, pivotal for more efficient assessment, prevention and intervention of SMA behaviours.

To the best of the authors’ knowledge, four studies to date have attempted to explore SMA profiles. Three of those have been conducted predominantly with European adolescent samples, and varied in terms of the type and number of profiles detected ( Bányai et al., 2017 , Brailovskaia et al., 2021 , Luo et al., 2021 , Cheng et al., 2022 ). The fourth was conducted with English speaking adults from the United Kingdom and the United States ( Cheng et al., 2022 ). Of extant studies, Bányai et al. (2017) identified three profiles varying quantitively (i.e., in terms of their SMA symptoms’ severity) across a low, moderate and high range. In contrast, Brailovskaia et al., 2021 , Luo et al., 2021 identified four and five profiles that varied both quantitatively and qualitatively in terms of the type of SMA symptoms reported. Brailovskaia et al., (2021) proposed the ‘low symptom’, ‘low withdrawal’ (i.e., lower overall SMA symptoms with distinctively lower withdrawal), ‘high withdrawal’ (i.e., higher overall SMA symptoms with distinctively higher withdrawal) and ‘high symptom’ profiles. Luo et al. (2021) supported the ‘casual’, ‘regular’, ‘low risk high engagement’, ‘at risk high engagement’ and ‘addicted’ user profiles, which demonstrated progressively higher SMA symptoms severity alongside significant differences regarding mood modification, relapse, withdrawal and conflict symptoms, that distinguished the low and high risk ‘high engagement’ profiles. Finally, considering the occurrence of different SMA profiles in adults, Cheng and colleagues, (2022), supported the occurrence of ‘no-risk’, ‘at risk’ and ‘high risk’ social media users applying in both US and UK populations, with the UK sample showing a lower proportion of the ‘no-risk’ profile (i.e. UK = 55 % vs US = 62.2) and a higher percentage of the high risk profile (i.e. UK = 11.9 % vs US = 9.1 %). Thus, considering the number of identified profiles best describing the population of social media users, Cheng and colleagues’ findings (2022) were similar to Bányai and colleagues’ (2017) suggestions for SMA behaviour profiles of adolescents. At this point it should be noted, that none of the four studies exploring SMA behaviours profiles to date has taken into consideration different profile parameterizations, meaning that potential differences in the heterogeneity/ variability of those classified within the same profile were not considered (e.g. some profiles maybe more loose/ inclusive than others; Bányai et al., 2017 , Brailovskaia et al., 2021 , Luo et al., 2021 , Cheng et al., 2022 ).

The lack of convergence regarding the optimum number and the description of SMA profiles occurring, as well as age, cultural and parameterization limitations of the four available SMA profiling studies, invites further investigation. This is especially evident in light of preliminary evidence confirming one’s SMA profile may link more to certain addictions over others ( Zarate et al., 2022 ). Indeed, those suffering from SMA behaviours have been shown to display heightened degrees of alcohol and drug use, a vulnerability to internet addiction in general, while presenting lower proneness towards exercise addiction and tobacco use ( Grant et al., 2010 , Anderson et al., 2017 , Duradoni et al., 2020 , Spilkova et al., 2017 ). In terms of gambling addiction, social media addicts display similar results on tests of value-based decision making as gambling addicts ( Meshi et al., 2019 ). Finally, regarding shopping addiction, the proliferation of advertisements for products online, and the ease of access via social media to online stores could be assumed to have an intensifying SMA effect ( Rose & Dhandayudham, 2014 ). Aside from these promising, yet relatively limited findings, the assessed connections between SMA and other addictions tend to be either addressed in isolation (e.g., SMA with gambling only and not multiple other addiction forms; Gainsbury et al., 2016a , Gainsbury et al., 2016b ) and in a variable (and not person) focused manner (e.g., higher levels of SMA relate with higher levels of drug addiction; Spilkova et al., 2017 ), which overlooks an individual’s profile. These profiles are vitally needed, as knowing the type of individual who may experience a series of disparate addictions is paramount for identifying at risk social media users and populations in need of more focused prevention/intervention programs ( Grant et al., 2010 ). Hence, using person focused methods such as latent profile(s) analysis (LPA) that address the ways in which distinct variations/profiles in SMA behaviours may occur, and how these relate with other addictions is imperative ( Lanza & Cooper, 2016 ).

1.2. Present study

To address this research priority, while considering SMA behaviours as being normally distributed (i.e., a minimum–maximum continuum) across the different profiles of users in the general population, the present Australian study uses a large community sample, solid psychometric measures and a sequence of differing in parameterizations LCA models aiming to: (a) advance past knowledge on the typology/structure of SMA symptom one experiences and: (b) innovatively explore the association of these typologies with a comprehensive list of addictive behaviours related to gaming, gambling, alcohol, smoking, drug abuse, sex (including porn), shopping, internet use, and exercise.

Based on Cheng and colleagues (2022) and Bányai and colleagues (2017), it was envisaged that three profiles arrayed in terms of ascending SMA symptoms’ severity would be likely identified. Furthermore, guided by past literature supporting closer associations between technological and behavioural addictions than with substance related addictions, it was hypothesized that those classified at higher SMA risk profiles would report higher symptoms of other technological and behavioural addictions, such as those related to excessive gaming and gambling, than with drug addiction ( Chamberlain and Grant, 2019 , Zarate et al., 2022 ).

2.1. Participants

The current study was conducted in Australia. Responses initially retrieved included 1097 participants. Of those, 129 were not considered for the current analyses. In particular, 84 respondents were classified as preview-only registrations and did not address any items, 5 presented with systematic response inconsistencies, and thus were considered invalid, 11 were excluded as potential bots, 11 had not provided their informed consent (i.e., did not tick the digital consent box, although they later addressed the survey), and 18 were taken out for not fulfilling age conditions (i.e., being adults), in line with the ethics approval received. Therefore, responses from 968 English-speaking adults from the general community were examined. An online sample of adult, English speaking participants aged 18 to 64 who were familiar with gaming [ N  = 968, M age  = 29.5, SD age  = 9.36, n males  = 622 (64.3 %), n females  = 315, (32.5 %), n trans/non-binary  = 26 (2.7 %), n queer  =  1 (0.1 %), n other  =  1 (0.1 %), n missing  =  3 (0.3 %)] was analysed. According to Hill (1998) random sampling error is required to lie below 4 %, that is satisfied by the current sample’s 3 % (SPH analytics, 2021). See Table 1 for participants’ sociodemographic information.

Socio-demographic and online use characteristics of participants.

Note: Percentages represent the percentage of that sex which is represented by any one grouping, rather than percentages of the overall population.

2.2. Measures

Psychometric instruments targeting sociodemographics, SMA and a semi-comprehensive range of behavioral, digital and substance addictions were employed. These instruments involved the Bergen Social Media Addiction Scale (BSMAS; Andreassen et al., 2012 ), the Internet Gaming Disorder 9 items Short Form (IGDS-SF9; Pontes & Griffiths, 2015 ), The Internet Disorder Scale (IDS9-SF; ( Pontes & Griffiths, 2016 ), the Online Gambling Disorder Questionnaire (IGD-Q; González-Cabrera et al., 2020 ), the 10-Item Alcohol Use Disorders Identification Test (AUDIT; Saunders et al., 1993 , the Five Item Cigarette Dependance Scale (CDS-5; Etter et al., 2003 ), the 10- item Drug Abuse Screening Test (DAST-10; Skinner, 1982 ), the Bergen-Yale Sex Addiction Scale (BYSAS; Andreassen et al., 2018), the Bergen Shopping Addiction Scale (BSAS; Andreassen et al., 2015) and the 6-item Revised Exercise Addiction Inventory (EAI-R; Szabo et al., 2019 ). Precise details of these measures, including values related to assumptions can be found in Table 2 .

Measure descriptions and internal consistency.

Note Table 2 : Streiner’s (2003) guidelines are used when measuring internal reliability, with Cronbachs Alpha scores in the range of 0.60–0.69 labelled ‘acceptable’, ranges between 0.70 and 0.89 labelled ‘good’ and ranges between 0.90 and 1.00 labelled ‘excellent’. Acceptable values of skewness fall between − 3 and + 3, and kurtosis is appropriate from a range of − 10 to + 10 ( Brown, 2006 ). OGD-G kurtosis (13.90) and skewness (3.45) exceeded the recommended limits ( Brown, 2006 ). However, LPA does not assume data distribution linearity, normality and or homogeneity ( Rosenberg et al., 2019 ). Considering aim B, related to detecting significant reported differences on measures for gaming, sex, shopping, exercise, gambling, alcohol, drug, cigarette and internet addiction symptoms respectively, anova results were derived after bootstrapping the sample 1000 times to ensure that normality assumptions were met. Case bootstrapping calculates the means of 1000 resamples of the available data and computes the results analysing these means, which are normally distributed ( Tong, Saminathan, & Chang, 2016 ).

2.3. Procedure

Approval was received from the Victoria University Human Research Ethics Committee (HRE20-169). Data was collected in August 2019 to August 2020 via an online survey link distributed via social media (i.e., Facebook; Instagram; Twitter), digital forums (i.e. reddit) and the Victoria University learning management system. Familiarity with gaming was preferred, so that associations with one’s online gaming patterns were studied. The link first took potential participants to the Plain Language Information Statement (PLIS) which informed on the study requirements and participants’ anonymity and free of penalty withdrawal rights. Digital provision of informed consent (i.e., ticking a box) was required by the participants before proceeding to the survey.

2.4. Statistical analyses

Statistical analyses were conducted via: a) R-studio for the latent profile(s) analyses (LPA) and; b) Jamovi for descriptive statistics and profiles’ comparisons. Regarding aim A, LPA identified naturally homogenous subgroups within a population ( Rosenberg et al., 2019 ). Through the TIDYLPA CRAN R package, a number of models varying in terms of their structure/parameterization and the number of ‘profiles’ were tested using the six BSMAS criteria/items as indicators ( Rosenberg et al., 2019 ; see Table 3 ).

LCA model parameterization characteristics.

Subsequently, the constructed models were compared regarding selected fit indices (i.e., Akaike Information Criterion (AIC) and the Bayesian Information Criterion (BIC), bootstrapped Lo-Mendel Rubin test (B-LMR or LRT), entropy and the N_Min; Rosenberg et al., 2019 ) 1 . This involved 1: Dismissing any models with N -Min’s equalling 0, as each profile requires at least one participant, 2: Dismissing models with entropy scores below 0.64 ( Tein et al., 2013 ), 3: Dismissing models with nonsignificant BLMR value, and 4: assessing the remaining models on their AIC/BIC looking for an elbow point in the decline or the lowest values.

Regarding aim B of the study, ANOVA with bootstrapping (1000x) was employed to detect significant profile differences regarding one’s gaming, sex, shopping, exercise, gambling, alcohol, drug, cigarette and internet addiction symptoms respectively.

All analyses’ assumptions were met with one exception 2 . The measure of Online Gambling disorder experience violated guidelines for the acceptable departure from normality and homogeneity ( Kim, 2013 ). Given this violation, results regarding gambling addiction should be considered with some caution.

3.1. Aim A: LPA of BSMAS symptoms

The converged models’ fit, varying by number of profiles and parametrization is displayed in Table 4 , with the CIP parameterisation presenting as the optimum (i.e. lower AIC and BIC, and 1–8 profiles converging; all CVDP, CIUP, CVUP models did not converge except the CVUP one profile). Subsequently, the CIP models were further examined via the TIDYLPA Mclust function (see Table 5 ). AIC and BIC decreased as the number of profiles increased. This flattened past 3 profiles (i.e., elbow point; Rosenberg et al., 2019 ). Furthermore, past 3 profiles, N -min reached zero, indicating profiles with zero participants in them – thus reducing interpretability. Lastly, the BLRT test reached non significance once the model had 4 profiles, again indicating the 3-profile model as best fitting. Therefore, alternative CIP -models were rejected in favour of the 3-profile one. This displayed a level of classification accuracy well above the suggested cut off point of 0.76 (entropy = 0.90; Larose et al., 2016 ), suggesting over 90 % correct classification ( Larose et al., 2016 ). Regarding the profiles’ proportions, counts revealed 33.6 % as profile 1, 52.4 % as profile 2, 14 % as profile 3.

Initial model testing.

Fit indices of cip models with 1–8 classes.

Table 6 and Fig. 1 present the profiles’ raw mean scores across the 6 BSMAS items whilst Table 7 and Fig. 2 present the standardised mean scores.

Raw Mean Scores and Standard Error of the 6 BSMAS Criteria Across the Three Classes/Profiles.

Raw symptom experience of the three classes.

Standardised mean scores of the 6 bsmas criteria Across the Three Classes/Profiles.

Note: For standard errors, see Table 6 .

Standardized symptom experience of the three classes.

Profile 1 scores varied from 1.74 to 2.98 raw and between 0.08 and 0.58 standard deviations above the sample mean symptom experience. In terms of plateaus and steeps, profile 1 displayed a raw score plateaus across symptoms 1–3 (salience, tolerance, mood modification), a decline in symptom 4 (relapse), and another plateau across symptoms 5–6 (withdrawal and conflict). It further displayed a standardized score plateau around the level of 0.5 standard deviations across symptoms 1–3 and a decline across symptoms 4–6. Profile 2 varied consistently between raw mean scores of 1 and 1.36 across the 6 SMA symptoms, and between −0.74 and −0.53 standard deviations from the sample mean with general plateaus in standardized score across symptoms 1–3 and 4–6. Finally, profile 3 mean scores varied between 3.02 and 3.95 raw and 1.26 to 1.88 standardized. Plateaus were witnessed in the raw scores across symptoms 1–3 (salience, tolerance, mood modification), a decline at symptom 4 (relapse), a relative peak at symptom 5 (withdrawal), and a further decline across symptom 6 (conflict). However, the standardized scores for profile 3 were relatively constant across the first four symptoms, before sharply reaching a peak at symptom 5 and then declining once more. Accordingly, the three profiles were identified as severity profiles ‘Low’ (profile 2), ‘Moderate’ (profile 1) and ‘High’ (profile 3) risk. Table 8 , Table 9 provide the profile means and standard deviations, as well as their pairwise comparisons across the series of other addictive behaviors assessed.

Post Hoc Descriptives across a semi-comprehensive list of addictions.

Post Hoc Comparisons of the SMA profiles revealed across the addictive behaviors measured.

3.2. Aim 2: BSMAS profiles and addiction risk/personal factors.

Table 8 , Table 9 display the Jamovi outputs for the BSMAS profiles and their means and standard deviations, as well as their pairwise comparisons across the series of other addictive behaviors assessed using ANOVA. Cohen’s (1988) benchmarks were used for eta squared values, with > 0.01 indicating small, >0.059 medium and > 0.138 large effects. ANOVA results were derived after bootstrapping the sample 1000 times to ensure that normality assumptions were met. Case bootstrapping calculates the means of 1000 resamples of the available data and computes the results analysing these means, which are normally distributed ( Tong et al., 2016 ). SMA profiles significantly differed across the range of behavioral addiction forms examined with more severe SMA profiles presenting consistently higher scores with a medium effect size regarding gaming ( F  = 57.5, p  <.001, η 2  = 0.108), sex ( F  = 39.53, p  <.001, η 2  = 0.076) and gambling ( F  = 40.332, p  <.001, η 2  = 0.078), and large effect sizes regarding shopping ( F  = 90.06, p  <.001, η 2  = 0.159) and general internet addiction symptoms ( F  = 137.17, p  <.001, η2 = 0.223). Only relationships of ‘medium’ size or greater were considered further in this analysis, though small effects were found with alcoholism ( F  = 11.34, p  <.001, η 2  = 0.023), substance abuse ( F  = 4.83, p  =.008, η 2  = 0.01) and exercise addiction ( F  = 5.415, p  =.005, η2 = 0.011). Pairwise comparisons consistently confirmed that the ‘low’ SMA profile scored significantly lower than the ‘moderate’ and the ‘high’ SMA profile’, and the ‘moderate’ SMA profile being significantly lower than the ‘high’ SMA profile across all addiction forms assessed (see Table 8 , Table 9 ).

4. Discussion

The present study examined the occurrence of distinct SMA profiles and their associations with a range of other addictive behaviors. It did so via uniquely combining a large community sample, measures of established psychometric properties addressing both SMA and an extensive range of other proposed substance and behavioral addictions, to calculate the best fitting model in terms of parameterization and profile number. A model of the CIP parameterization with three profiles was supported by the data. The three identified SMA profiles ranged in terms of severity and were labeled as ‘low’ (52.4 %), ‘moderate’ (33.6 %) and ‘high’ (14 %) SMA risk. Membership of the ‘high’ SMA risk profile was shown to link with significantly higher reported experiences of Internet and shopping addictive behaviours, and moderately with higher levels of addictive symptoms related to gaming, sex and gambling.

4.1. Number and variations of SMA profiles

Three SMA profiles, entailing ‘low’ (52.4 %), ‘moderate’ (33.6 %) and ‘high’(14 %) SMA risk were supported, with symptom 5 – withdrawal – displaying the highest inter-profile disparities. These results help clarify the number of SMA profiles in the population, as past findings were inconsistent supporting either 3, or 4 or 5 SMA profiles ( Bányai et al., 2017 , Brailovskaia et al., 2021 , Luo et al., 2021 ), as well as the nature of the differences between these profiles (i.e. quantitative: “how much/high one experiences SMA symptoms” or qualitative: “the type of SMA symptoms one experiences”). Our findings are consistent with the findings of Bányai and colleagues (2017) and Cheng and colleagues (2022) indicating a unidimensional experience of SMA (i.e., that the intensity/severity an individual reports best defines their profile membership, rather than the type of SMA symptoms) with three profiles ranging in severity from ‘low’ to ‘moderate’ to ‘high’ and those belonging at the higher risk profiles being the minority. Conversely, these results stand in opposition with two past studies identifying profiles that varied qualitatively (i.e., specific SMA symptoms experienced more by certain profiles) and suggesting the occurrence of 4 and 5 profiles respectively ( Brailovskaia et al., 2021 , Luo et al., 2021 ). Such differences might be explained by variations in the targeted populations of these studies. Characteristics such as gender, nationality and age all have significant effects on how and why social media is employed ( Andreassen et al., 2016 ; Hsu et al., 2015 ; Park et al., 2015 ). Given that the two studies in question utilized European, adolescent samples, the difference in the culture and age of our samples may have produced our varying results, ( Brailovskaia et al., 2021 , Luo et al., 2021 ). Comparability issues may also explain these results, given the profiling analyses implemented in the studies of Brailovskaia and colleagues, (2021), as well as Luo and colleagues (2021) did not extensively consider different profiles parameterizations, as the present study and Cheng et al. (2022) did. Furthermore, the results of this study closely replicated those of the Cheng et al., (2022) study, with both studies identifying a near identical pattern of symptom experience across three advancing levels of severity. This replication of results may indicate their accuracy, strengthening the validity of SMA experience models involving 3 differentiated profiles of staggered severity. Both our findings and Cheng et al.’s findings indicate profiles characterized by higher levels of cognitive symptoms (salience, withdrawal and mood modification) for each class when compared to their experience of behavioral symptoms (Relapse, withdrawal, conflict; Cheng et al., 2022 ). Further research may focus on any potentially mediating/moderating factors that may be interfering, and potentially further replicate such results, proving their reliability. Furthermore, given that past studies (with different results) utilized European, adolescent samples, cultural and age comparability limitations need to be considered and accounted for in future research ( Bányai et al., 2017 , Brailovskaia et al., 2021 ; Cheng et al., 2022 ).

Regarding withdrawal being the symptom of highest discrepancy between profiles, findings suggest that it may be more SMA predictive, and thus merit specific assessment or diagnostic attention, aligning with past literature ( Bányai et al., 2017 , Luo et al., 2021 , Brailovskaia et al., 2021 , Smith and Short, 2022 ). Indeed, the experience of irritability and frustration when abstaining from usage has been shown to possess higher differentiation power regarding diagnosing and measuring other technological addictions such as gaming, indicating the possibility of a broader centrality to withdrawal across the constellation of digital addictions ( Gomez et al., 2019 ; Schivinski et al., 2018 ).

Finally, the higher SMA risk profile percentage in the current study compared with previous research [e.g., 14 % in contrast to the 4.5 % ( Bányai et al., 2017 ), 4.2 % ( Luo et al., 2021 ) and 7.2 % ( Brailovskaia et al., 2021 )] also invites significant plausible interpretations. The data collection for the present Australian study occurred between August 2019 to August 2020, while Bányai and their colleagues (2017) collected their data in Hungary in March 2015, and Brailovskaia and their colleagues (2021) in Lithuania and Germany between October 2019 and December 2019. The first cases of the COVID-19 pandemic outside China were reported in January 2020, and the pandemic isolation measures prompted more intense social media usage, to compensate for their lack of in person interactions started unfolding later in 2020 ( Ryan, 2021 , Saud et al., 2020 ). Thus, it is likely that the higher SMA symptom scores reported in the present study are inflated by the social isolation conditions imposed during the time the data was collected. Furthermore, the present study involves an adult English-speaking population rather than European adolescents, as the studies of Bányai and their colleagues (2017) and Brailovskaia and their colleagues (2021). Thus, age and/or cultural differences may explain the higher proportion of the high SMA risk profile found. For instance, it is possible that there may be greater SMA vulnerability among older demographics and/or across countries. The explanation of differences across counties is reinforced by the findings of Cheng and colleagues (2022) who assessed and compared UK and US adult populations, the first is less likely, as younger age has been shown to relate to higher SMA behaviors ( Lyvers et al., 2019 ). Overall, the present results closely align with that of Cheng and colleagues (2022), who also collected their data during a similar period (between May 18, 2020 and May 24, 2020) from English speaking countries (as the present study did). They, in line with our findings, also supported the occurrence of three SMA behavior profiles, with the low risk profile exceeding 50 % of the general population and those at higher risk ranging above 9 %.

4.2. Concurrent addiction risk

Considering the second study aim, ascending risk profile membership was strongly related to increased experiences of internet and shopping addiction, while it moderately connected with gaming, gambling and sex addictions. Finally, it weakly associated with alcohol, exercise and drug addictions. These findings constitute the first semi-comprehensive cross-addiction risk ranking of SMA high-risk profiled individuals, allowing the following implications.

Firstly, no distinction was found between the so called “technological” and other behavioral addictions, potentially contradicting prior theory on the topic ( Gomez et al., 2022 ). Typically, the abuse of internet gaming/pornography/social media, has been classified as behavioral addiction ( Enrique, 2010 , Savci and Aysan, 2017 ). However, their shared active substance – the internet – has prompted some scholars to suggest that these should be classified as a distinct subtype of behavioral addictions named “technological/ Internet Use addictions/disorders” ( Savci & Aysan, 2017 ). Nevertheless, the stronger association revealed between the “high” SMA risk profile and shopping addictions (not always necessitating the internet), compared to other technology related addictions, challenges this conceptual distinction ( Savci & Aysan, 2017 ). This finding may point to an expanding intersection between shopping and SMA, as an increasing number of social media platforms host easily accessible product and services advertising channels (e.g., Facebook property and car selling/marketing groups, Instagram shopping; Rose & Dhandayudham, 2014 ). In turn, the desire to shop may prompt a desire to find these services online, share shopping endeavors with others or find deals one can only access through social media creating a reciprocal effect ( Rose & Dhandayudham, 2014 ). This possibility aligns with previous studies assuming reciprocal addictive co-occurrences ( Tullett-Prado et al., 2021 ). This relationship might also be exacerbated by shared causal factors underpinning addictions in general, such as one’s drive for immediate gratification and/or impulsive tendencies ( Andreassen et al., 2016 ; Niedermoser et al., 2021 ). Although such interpretations remain to be tested, the strong SMA and shopping addiction link evidenced suggests that clinicians should closely examine the shopping behaviors of those suffering from SMA behaviours, and if comorbidity is detected – address both addictions concurrently ( Grant et al., 2010 , Miller et al., 2019 ). Conclusively, despite some studies suggesting the distinction between technological, and especially internet related (e.g., SMA, internet gaming), addictions and other behavioral addictions ( Gomez et al., 2022 , Zarate et al., 2022 ), the current study’s high risk SMA profile associations appear not to differentiate based on the technological/internet nature that other addictions may involve.

Secondly, results suggest a novel hierarchical list of the types of addictions related to the higher SMA risk profile. While previous research has established links between various addictive behaviors and SMA (i.e., gaming and SMA; Wang et al., 2015 ), these have never before - to the best of the authors’ knowledge – been examined simultaneously allowing their comparison/ranking. Therefore, our findings may allow for more accurate predictions about the addictive comorbidities of SMA, aiding in SMA’s assessment and treatment. For example, Internet, shopping, gambling, gaming and sex addictions were all shown to more significantly associate with the high risk SMA profile than exercise and substance related addictive behaviors ( King et al., 2014 ; Gainsbury et al., 2016a ; Gainsbury et al., 2016b ; Rose and Dhandayudham, 2014 , Kamaruddin et al., 2018 , Leung, 2014 ). Thus, clinicians working with those with SMA may wish to screen for gaming and sex addictions. Regardless of the underlying causes, this hierarchy provides the likelihood of one addiction precipitating and perpetuating another in a cyclical manner, guiding assessment, prevention, and intervention priorities of concurrent addictions.

Lastly, these results indicate a lower relevance of the high risk SMA profile with exercise/substance addictive behaviors. Considering excessive exercise, our study reinforces literature indicating decreased physical activity among SMA and problematic internet users in general ( Anderson et al., 2017 , Duradoni et al., 2020 ). Naturally, those suffering from SMA behaviours spend large amounts of time sedentary in front of a screen, precluding excessive physical activities. Similarly, the lack of a significant relationship between tobacco abuse and SMA has also been identified priori, perhaps due to the cultural divide between social media and smoking in terms of their acceptance by wider society and of the difference in their users ( Spilkova et al., 2017 ). Contrary to expectations, there were weak/negligible associations between the high SMA risk profile with substance and alcohol abuse behaviours. This finding contradicts current knowledge supporting their frequent comorbidity ( Grant et al., 2010 , Spilkova et al., 2017 ; Winpenny et al., 2014 ). This finding may potentially be explained by individual differences between these users, as while one can assume many traits are shared between those vulnerable to substances and SMA, these may be expressed differently. For example, despite narcissism being a common addiction risk factor, its predictive power is mediated by reward sensitivity in SMA, where in alcoholism and substances, no such relationship exists ( Lyvers et al., 2019 ). Perhaps the constant dopamine rewards and the addictive reward schedule of social media targets this vulnerability in a way that alcoholism does not. Overall, one could assume that the associations between SMA and less “traditionally” (i.e., substance related; Gomez et al., 2022 ) viewed addictions deserves more attention. Thus, future research is recommended.

4.3. Limitations and future direction

The current findings need to be considered in the light of various limitations. Firstly, limitations related to the cross-sectional, age specific and self-report surveyed data are present. These methodological restrictions do not allow for conclusions regarding the longitudinal and/or causal associations between different addictions, nor for generalization of the findings to different age groups, such as adolescents. Furthermore, the self-report questionnaires employed may accommodate subjectivity biases (e.g., subjective and/or false memory recollections; Hoerger & Currell, 2012 ; Sun & Zhang, 2020 The latter risk is reinforced by the non-inclusion of social desirability subscales in the current study, posing obstacles in ensuring participant responses are accurate.

Additionally, there is a conceptual overlap between SMA and Internet Addiction (IA), which operates as an umbrella construct inclusive of all online addictions (i.e., irrespective of the aspect of the Internet being abused; Anderson et al., 2017 , Savci and Aysan, 2017 ). Thus, caution is warranted considering the interpretation of the SMA profiles and IA association, as SMA may constitute a specific subtype included under the IA umbrella ( Savci & Aysan, 2017 ). However, one should also consider that: (a) SMA, as a particular IA subtype is not identical to IA ( Pontes, & Griffiths, 2014 ); and (b) recent findings show that IA and addictive behaviours related to specific internet applications, such as SMA, could correlate with different types of electroencephalogram [EEG] activity, suggesting their neurophysiological distinction (e.g. gaming disorder patients experience raised delta and theta activity and reduced beta activity, while Internet addiction patients experience raised gamma and reduced beta and delta activity; Burleigh et al., 2020 ). Overall, these advocate in favour of a careful consideration of the SMA profiles and IA associations.

Finally, the role of demographic differences, related to one’s gender and age, which have been shown to mediate the relationship between social media engagement and symptoms of other psychiatric disorders ( Andreassen et al., 2016 ) have not been attended here.

Thus, regarding the present findings and their limitations, future studies should focus on a number of key avenues; (1) achieving a more granular understanding of SMA’s associations with comorbid addictions via case study or longitudinal research (e.g., cross lag designs), (2) further clarifying the nature of the experience of SMA symptoms, (3) investigating the link between shopping addiction and SMA, as well as potential interventions that target both of these addictions simultaneously and, (4) attending to gender and age differences related to the different SMA risk profiles, as well as how these may associate with other addictions.

5. Conclusion

The present study bears significant implications for the way that SMA behaviours are assessed among adults in the community and subsequently addressed in adult clinical populations. By profiling the ways in which SMA symptoms are experienced, three groups of adult social media users, differing regarding the reported intensity of their SMA symptoms were revealed. These included the ‘low’ (52.4 %), ‘moderate’ (33.6 %) and ‘high’ (14 %) SMA risk profiles. The high SMA risk profile membership was strongly related to increased rates of reported internet and shopping related addictive behaviours, moderately associated with gaming, gambling and sex related addictive behaviours and weakly associated with alcohol, exercise and drug related addictive behaviours, to the point that such associations were negligible at most. These results enable a better understanding of those experiencing higher SMA behaviours, and the introduction of a risk hierarchy of SMA-addiction comorbidities that needs to be taken into consideration when assessing and/or treating those suffering from SMA symptoms. Specifically, SMA and its potential addictive behaviour comorbidities may be addressed with psychoeducation and risk management techniques in the context of SMA relapse prevention and intervention plans, with a greater emphasis on shopping and general internet addictive behaviours. Regarding epidemiological implications, the inclusion of 14 % of the sample in the high SMA risk profile implies that while social media use can be a risky experience, it should not be over-pathologized. More importantly, and provided that the present findings are reinforced by other studies, SMA awareness campaigns might need to be introduced, while regulating policies should concurrently address the risk for multiple addictions among those suffering from SMA behaviours.

Note 1: Firstly, results were compared across all converged models. In brief, the AIC and BIC are measures of the prediction error which penalize goodness of fit by the number of parameters to prevent overfit, models with lower scores are deemed better fitting ( Tein et al., 2013 ). Of the 16 possible models, the parameterization with the most consistently low AIC’s and BIC’s across models with 1–8 profiles were chosen, eliminating 8 of the possible models. Subsequently, the remaining models were more closely examined through TIDYLPA using the compare solutions command, with the. BLMR operating as a direct comparison between 2 models (i.e. the model tested and a similar model with one profile less) on their relative fit using likelihood ratios. A BLMR based output p value will be obtained for each comparison pair with lower p-values corresponding to the greater fit among the models tested (i.e. if BLMR p >.05, the model with the higher number of profiles needs to be rejected; Tein et al., 2013). Entropy is an estimate of the probability that any one individual is correctly allocated in their profile/profile. Entropy ranges from 0 to 1 with higher scores corresponding with a better model ( Tein et al., 2013 ; Larose et al., 2016 ). Finally, the N_min represents the minimum proportion of sample participants in any one presentation profile and aids in determining the interpretability/parsimony of a model. If N_min is 0, then there is a profile or profilees in the model empty of members. Thus, the interpretability and parsimony of the model is reduced ( CRAN, 2021 ). These differing fit indices were weighed up against eachother in order to identify the best fitting model (Akogul & Erisoglu, 2017). This best fitting model was subsequently applied to the datasheet, and then the individual profilees examined through the use of descriptive statistics in order to identify their characteristics.

Note 2: With regards to the assumptions of the LPA Model, as a non-parametric test, no assumptions were made regarding the distribution of data. With regards to the subsequent ANOVA analyses, 2 assumptions were made as to the nature of the distribution. Homogeneity of variances and Normality. Thus, the distribution of the data was assessed via Jamovi. Skewness and Kurtosis for all measures employed in the ANOVA analyses. Skewness ranged from 0.673 to 2.49 for all variables bar the OGD-Q which had a skewness of 3.45. Kurtosis ranged from 0.11 to 6 for variables bar the OGD-Q which had a kurtosis of 13.9. Thus, all measures excepting the OGD-Q sat within the respective acceptable ranges of + 3 to −3 and + 10 to −10 recommended by Brown and Moore (2012).

Dr Vasileios Stavropoulos received funding by:

The Victoria University, Early Career Researcher Fund ECR 2020, number 68761601.

The Australian Research Council, Discovery Early Career Researcher Award, 2021, number DE210101107.

Ethical Standards – Animal Rights

All procedures performed in the study involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. This article does not contain any studies with animals performed by any of the authors. Thus, the present study was approved by the Human Ethics Research Committee of Victoria University (Australia).

Informed consent

Informed consent was obtained from all individual participants included in the study.

Confirmation statement

Authors confirm that this paper has not been either previously published or submitted simultaneously for publication elsewhere.

Publication

Authors confirm that this paper is not under consideration for publication elsewhere. However, the authors do disclose that the paper has been considered elsewhere, advanced to the pre-print stage and then withdrawn.

Authors assign copyright or license the publication rights in the present article.

Availability of data and materials

Data is deposited as a supplementary file with the current document.

CRediT authorship contribution statement

Deon Tullett-Prado: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Data curation. Vasileios Stavropoulos: Supervision, Resources, Funding acquisition, Project administration. Rapson Gomez: Supervision, Resources. Jo Doley: Supervision, Resources.

Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Biographies

Deon Tullett-Prado: Deon Tullett-Prado is a PhD candidate and emerging researcher in the area of behavioral addictions and in particular Internet Gaming Disorder. His expertise involves advanced statistical analysis skills and innovative techniques regarding population profiling.

Dr Vasileios Stavropoulos: Dr Vasileios Stavropoulos is a member of the Australian Psychological Society (APS) and a registered psychologist endorsed in Clinical Psychology with the Australian Health Practitioner Regulation Authority (AHPRA). Vasileios' research interests include the areas of Behavioral Addictions and Developmental Psychopathology. In that context, Vasileios is a member of the European Association of Developmental Psychology (EADP) and the EADP Early Researchers Union. Considering his academic collaborations, Vasileios maintains his research ties with the Athena Studies for Resilient Adaptation Research Team of the University of Athens, the International Gaming Centre of Nottingham Trent University, Palo Alto University and the Korean Advanced Institute of Science and Technology. Vasileios has received the ARC DECRA award 2021.

Dr Rapson Gomez: Rapson Gomez is professor in clinical psychology who once directed clinical training at the School of Psychology, University of Tasmania (Hobart, Australia). Now he focuses on research using innovative statistical techniques with a particular focus on ADHD, biological methods of personality, psychometrics and Cyberpsychology.

Dr Jo Doley: A lecturer at Victoria University, Dr Doley has a keen interest in the social aspects of body image and eating disorders. With expertise in a variety of quantitative methodologies, including experimental studies, delphi studies, and systematic reviews, Dr Doley has been conducting research into the ways that personal characteristics like sexual orientation and gender may impact on body image. Furthermore, in conjunction with the cyberpsychology group at VU they have been building a new expertise on digital media and it’s potential addictive effects.

Appendix A Supplementary data to this article can be found online at https://doi.org/10.1016/j.abrep.2023.100479 .

Appendix A. Supplementary material

The following are the Supplementary data to this article:

Data availability

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