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The purpose of this study was to understand better how social media and cyberbullying might be warning signs of deeper emotional struggles, especially in younger students. The focus of the study was on teens and young adults and the relationship between cyberbullying and mental health issues like depression and low self-esteem. A total of 804 high school students and 760 college students were surveyed using quantitative self-report questions focused on the relationship between online behavior and emotional well-being. They used path analysis to examine the relationships between different factors.

Kırcaburun, K., Kokkinos, C. M., Demetrovics, Z., Király, O., Griffiths, M. D., & Çolak, T. S. (2019). Problematic online behaviors among adolescents and emerging adults: Associations between cyberbullying perpetration, problematic social media use, and psychosocial factors. International Journal of Mental Health and Addiction, 17(4), 891–908. https://doi.org/10.1007/s11469-018-9894-8

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Fanni Bányai,

Ágnes Zsila,

Orsolya Király,

Aniko Maraz,

Zsuzsanna Elekes,

Mark D. Griffiths,

Cecilie Schou Andreassen,

Zsolt Demetrovics

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Abstract

Despite social media use being one of the most popular activities among adolescents, prevalence estimates among teenage samples of social media (problematic) use are lacking in the field. The present study surveyed a nationally representative Hungarian sample comprising 5,961 adolescents as part of the European School Survey Project on Alcohol and Other Drugs (ESPAD). Using the Bergen Social Media Addiction Scale (BSMAS) and based on latent profile analysis, 4.5% of the adolescents belonged to the at-risk group, and reported low self-esteem, high level of depression symptoms, and elevated social media use. Results also demonstrated that BSMAS has appropriate psychometric properties. It is concluded that adolescents at-risk of problematic social media use should be targeted by school-based prevention and intervention programs.

Citation: Bányai F, Zsila Á, Király O, Maraz A, Elekes Z, Griffiths MD, et al. (2017) Problematic Social Media Use: Results from a Large-Scale Nationally Representative Adolescent Sample. PLoS ONE 12(1): e0169839. https://doi.org/10.1371/journal.pone.0169839

Editor: Susana Jiménez-Murcia, Hospital Universitari de Bellvitge, SPAIN

Received: November 1, 2016; Accepted: December 23, 2016; Published: January 9, 2017

Copyright: © 2017 Bányai et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: Data are available at DOI: 10.6084/m9.figshare.4479434.

Funding: This study was supported by the Hungarian National Research, Development and Innovation Office (Grant numbers: K111938, K111740). Ágnes Zsila was supported by the New National Excellence Program awarded by the Ministry of Human Resources. The funding institutions had no role in the study design or the collection, analysis and interpretation of the data, writing the manuscript, or the decision to submit the paper for publication.

Competing interests: The authors have declared that no competing interests exist.

Introduction

Social media use

Social media use is currently one of the most popular leisure activities among adolescents (e.g., [ 1- 3]). Social media (e.g., Facebook, Instagram, Snapchat, etc.) host virtual communities where users can create individual public and/or private profiles [ 4- 6]. Users can access social media on different platforms (mobile or computer devices), for different activities (e.g., interacting with real-life friends, meeting others based on shared interest, chatting, mailing, sharing or creating pictures / videos, blogging, dating, playing games, gambling; [ 7- 9]).

Facebook is one of the most popular social media among 13-17 years old adolescents in the USA [ 2]. According to a recent report, 71% of teenage social media users access more than one social media and 24% of adolescents are "almost constantly" online due to the widespread use and popularity of smartphones [ 2]. Furthermore, there is an increasing interest to explore and assess the characteristics and prevalence of problematic/excessive use of social media (e.g., [ 4, 10- 14]).

Problematic social media use

To date, there is no consensus among researchers regarding the definition of problematic social media use due to the conceptual confusion surrounding the classification of problematic internet use [ 15, 16]. Negative outcomes triggered by the excessive use of social media may have a detrimental effect on the personal, social, and/or professional lives of the users [ 8, 13, 17- 20]. Lee, Cheung, and Thadani [ 21] argued that obsessive Facebook users had troubles in work, academic performance. and interpersonal relationships. For instance, Pantic and Damjanovic [ 22], Wegmann and Stodt [ 16], and Andreassen and Billieux [ 23] reported a significant positive correlation between depression symptoms and social media use, while Malik and Khan [ 24] found negative relationship between self-esteem and high levels of social media use.

Due to the lack of consistency in empirical studies, diagnosis of internet-related disorders has yet to be established based on the aforementioned theoretical constructs. Internet Use Disorder was suggested for consideration in the latest (fifth) edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-5; [ 25]). However, only one internet-related disorder-Internet Gaming Disorder-was included in Section 3 of the DSM-5. Another problem is that various synonyms of problematic social media use exist in the literature with different diagnostic suggestions including (among others) Facebook dependence [ 26], Facebook addiction [ 10], social networking addiction [ 27], Twitter addiction [ 28], social media addiction [ 11], and Social Media Disorder [ 29].

Different theoretical models provide explanations for the development of problematic social media use (e.g., cognitive-behavioral, social skill, or socio-cognitive models; [ 30]. These theoretical models have been developed from a clinical perspective, while the biopsychosocial model concerns behavioral addictions in general [ 14]. According to the biopsychosocial model [ 31], problematic social media use can be determined by a range of addiction symptoms including: mood modification (i.e., excessive social media use leading to specific changes in mood states), salience (i.e., total preoccupation with social media use), tolerance (i.e., increasing amounts of time using social media), withdrawal symptoms (i.e., negative feelings and psychological symptoms such as irritability, anxiety when social media use is restricted), conflict (i.e., interpersonal problems as a direct result of social media usage), and relapse (i.e., returning to excessive social media use after a period of abstinence).

Assessing problematic social media use

To obtain a reliable prevalence rate of problematic social media usage, it is important to use psychometrically valid measurement tools. Due to the problem of inconsistencies regarding the definition of problematic, excessive, or addictive social media use, there is also a lack of reliable and valid psychometric scales to assess the phenomenon of problematic social media use. More specifically, the existing assessment tools are based on different diagnostic suggestions such as problematic internet use (e.g., Internet Addiction Test; [ 32- 34], Internet Gaming Disorder [ 29]), or other aspects of addictive tendencies (e.g., withdrawal, loss of control, salience; [ 35, 36]. In addition, some of the measurement tools focus only on specific social media (e.g., Facebook; [ 14] such as the Facebook Addiction Symptoms Scale [ 37], the Facebook Addiction Scale [ 38], the Bergen Facebook Addiction Scale [ 10], and the Facebook Intrusion Questionnaire [ 39]).

Although the most recent data show that Facebook is the most popular and frequently used social media among adolescents [ 2], empirical research has shown that adolescents use more than one social media frequently (e.g., [ 2]). Therefore, the assessment tools are unable to follow the ever-changing trends in the area of social media use. Considering the increased usage of various social media among adolescents [ 1- 3, 5] the questionnaires should assess all available social media and the total range of activities on these social media instead of one specific social media such as Facebook [ 14].

Prevalence of problematic social media use

It is difficult to estimate the prevalence of problematic social media use due to the use of various assessment tools and the lack of a consensual definition of problematic social media use. Furthermore, recent research has demonstrated that problematic social media use has a higher prevalence among female users than males [ 11, 13, 40, 41]. Unfortunately, in studies that have assessed different aspects of problematic social media use, the gender distribution was usually frequently imbalanced in that women were typically over-represented [ 11, 12, 15, 26, 29, 37, 42- 44], and may be explained by the higher willingness of females to participate in such studies.

Due to different theoretical frameworks and psychometric assessments, the prevalence of problematic social media use might be underestimated or overestimated. Previous studies have reported different prevalence rates relating to problematic social media use. For instance, Olowu and Seri [ 44] reported a prevalence rate of 2.8% of addicted social media use among college students, while Jafarkarimi and Sim [ 43] reported a prevalence rate of 47% being addicted to Facebook among a sample of college students. Explanations for the large difference in problematic social media use prevalence rates might be the non-representative (self-selected and typically small participant) samples and different cultural groups examined (e.g., Chinese, Australian, Nigerian college students, Dutch adolescents [ 12, 15, 26, 29, 37, 42- 44]. Moreover, to date, there has only been one nationwide survey assessing problematic (ie., addictive) social media use [ 11] that examined the associations between problematic social media use, narcissism, and self-esteem, and between problematic use of social media, attention-deficit/hyperactivity, obsessive-compulsiveness, anxiety, and depression on cross-sectional convenience sample of 23,532 Norwegians (although the sample was not nationally representative).

Furthermore, no studies have examined the prevalence of problematic social media use utilizing a representative adolescent sample. Furthermore, only a few studies exist concerning problematic social media use among adolescents (e.g., [ 29]). Previous studies have reported an increased popularity of social media use among adolescents [ 1- 4, 6] and the increased number of adolescent social media users could explain the higher prevalence of problematic usage in this group [ 4, 6]. Consequently, the aim of the present study was twofold:

1. To test the psychometric properties of the Bergen Social Media Addiction Scale (BSMAS) using a nationally representative (Hungarian) adolescent sample.

2. To assess the prevalence of problematic social media use in a nationally representative adolescent sample.

Methods

Participants and procedure

The data were collected in March 2015 as part of the European School Survey Project on Alcohol and Other Drugs (ESPAD; [ 45]) that included a nationally representative adolescent sample. The target population was adolescents aged 16 years. In 2015, Hungary included a short section to assess internet and social media use in addition to the original questionnaire developed by the ESPAD Committee.

To obtain a representative sample, two different grades (9 th-10 th) were included in the Hungarian data collection, each containing a proportion of the target population. To reduce sampling error, the grades were divided into non-overlapping, homogeneous subgroups. The variables to ensure the representativeness of the adolescent sample were as follows: region (central/western/eastern Hungary), grade (9th, 10th), and type of class (secondary general, secondary vocational, vocational classes). The data were collected anonymously from the students in the classrooms of the schools by research assistants.

The refusal rate was 7% on the level of the primary sampling unit (i.e., classes) that led to skewed nonresponse. To match the composition of the respondents with the sampling frame, data were weighted by strata with the matrix weighting method recommended by the Education Information System 2014/2015 (KIR-STAT; Elekes, 2015). The total sample consisted of 6,664 participants (50.94% male). The youngest participants were 15 years old, while the oldest were 22 years (mean age 16.62 years; SD = 0.96). The wide age range was due to a very small number of older students still attending the 9 th or 10 th grades at the age of 19 years or older at the time of data collection. The questions concerning internet use and social media use were included for this nationally representative sample of 9 th-10 th graders in secondary general and secondary vocational schools. Participant data with severe incompleteness or inconsistencies were excluded (3.72% of the sample), in addition to those participants who did not use the internet and/or any social media (an additional 6.83% of the original sample). After removing these participants, the final sample size was 5,961 (89.45% of the total sample).

This study was approved by the Scientific Ethical Committee of Corvinus University of Budapest. The study design was based on an international protocol approved by the European School Survey Project on Alcohol and Other Drugs (ESPAD) Assembly, which was conducted in full compliance with the principles expressed in the Declaration of Helsinki. Written informed consent was requested from both the students and their parents (passive on behalf of their children).

Measures

Socio-demographics questions.

Information regarding gender, age, grade, and residence were collected.

Weekly social media use.

To assess the adolescents' weekly time spent on social media on computer or other devices (e.g., handled devices) two variables were combined: (i) 'The last 7 days how many days did you use the internet for social networking?' (Categories were 'never', '1 day', '2 days', ...'7 days'); (ii) 'In the last 30 days on an average day how many hours did you use the internet for social networking?' (Categories were 'I don't use', 'less than half an hour', '1 hour', '2-3 hours', '4-5 hours' and 'more than 6 hours').

Bergen Social Media Addiction Scale (BSMAS).

To assess problematic social media use, the Bergen Social Media Addiction Scale (BSMAS; [ 11]) was used. The 6-item scale was adapted from the previously validated Bergen Facebook Addiction Scale (BFAS; [ 10]). The original scale specifically assessed problematic Facebook use during the last year. The scale incorporated the theoretical framework of the addiction components of the biopsychosocial model [ 31]. The BFAS was developed by selecting the items with the highest possible factor loadings for each component (i.e., salience, mood modification, tolerance, withdrawal symptoms, conflict, and relapse) from an item-pool of 18 initial items. In the present study, the Bergen Social Media Addiction Scale (BSMAS) which is based on rephrasing of the BFAS, was to assess social media use in general over the past 12 months. The scale was translated to Hungarian and then back-translated by independent translators. The back-translation was then compared with the original scale and adjustments were made as necessary. The items are answered on a 5-point scale ("never" to "always"). The Cronbach's alpha of the translated BSMAS was .85 in the present sample.

Rosenberg's Self-Esteem Scale.

Self-worth was assessed by the Hungarian version of the Rosenberg's Self-Esteem Scale (RSES-HU; [ 46], Hungarian version, [ 47]). The RSES assessed global self-esteem (i.e., feeling of self-worth and self-acceptance) with 10 items on a 4-point scale ("strongly agree" to "strongly disagree"). The score range is between 10-40 and the higher the score, the higher the self-esteem. Cronbach's alpha was 0.87 in the present sample.

Center of Epidemiological Studies Depression-Scale.

Depressive mood was assessed with the 6-item short-form of the Center of Epidemiological Studies Depression-Scale (CES-D; [ 48]). The scale assesses the level of depressive symptoms but it was not designed to diagnose clinical depression. The instrument was translated and then back-translated by Hungarian experts in the addiction field. The back-translation was then compared to the original instrument and adjustments were made where necessary. The items of CES-D were answered on a 4-point scale ("rarely or never" to "most of the time"). The score range is 4-24, and a higher score indicates higher level of depressive symptoms. Cronbach's alpha was 0.84 in the present sample.

Statistical analysis

To test the one-factor model of the BSMAS, confirmatory factor analysis (CFA) was performed with maximum likelihood estimation with robust standard error (MLR) in Mplus 7.3 [ 49]. To evaluate the model fit, a p-value of Chi-square (χ ²) higher than .05 was used for the test of close fit [ 50]. Additional fit indices were also included: the comparative fit index (CFI), the Tukey-Lewis Fit Index (TLI), the root mean square error of approximation (RMSEA) and its 95% confidence interval (90% CI), and standardized root mean square residual (SRMR). To indicate a good fit of the model, both CFI and TLI values have to be over than .90 or over .95 [ 51], while the values of RMSEA and SRMR should be less than .05 and .10 respectively [ 51- 53].

In order to identify the groups of adolescents with high risk of problematic social media use, a mixture modeling technique called latent profile analysis (performed in Mplus 7.3) was used. Latent profile analysis is a mixture modeling technique to identify groups of people (categorical output variable of the analysis) according to their responses to certain continuous variables (in the present study's case, the scores given on the six items of the BSMAS). Individuals with similar responses are classified in the same group [ 54]. Latent profile analysis was performed with 2 to 4 classes in the full sample (n = 5,961). To determine the number of latent classes, several indices were used, such as the measures of parsimony of each model (i.e., Akaike Information Criteria-AIC, Bayesian Information Criteria-BIC, and the Sample Size Adjusted Bayesian Information Criteria-SSABIC). The lower values on these indicators, the more parsimonious the model. The entropy criterion and the interpretability of clusters were also examined. In the final determination of the number of classes, the likelihood-ratio difference test (Lo-Mendell-Rubin Adjusted LRT Test) was used that statistically compares the fit of the estimated model with a model having one less class than the estimated one. The p-value of less than .05 suggests the tested model fits better than the model with one less class [ 49].

To test the construct validity of the BSMAS, the LPA classes were compared along a number of variables relevant to the phenomenon of SOCIAL MEDIA use (i.e., gender, SOCIAL MEDIA use hours/ week, level of self-esteem, level of depression). For these comparisons, Wald's Chi-square test of mean equality for latent class predictors in mixture modeling was used because it takes into consideration the probabilistic nature of the LPA classes (for description of the analysis, see www.statmodel.com/download/meantest2.pdf).

To determine the optimal cut-off point for the BSMAS, a sensitivity analysis was performed and the group with the highest risk of problematic social media use (based on the results of the LPA analysis) was considered as the "gold standard". The present authors are aware that this method does not replace the clinical validation process, however, the authors believe that this is better than using completely ad-hoc cut-off points as several other studies do. The sensitivity, specificity, positive and negative predictive value (PPV and NPV, respectively), and the accuracy of each cut-off threshold were calculated and compared to identify the cut-off value with the best indicators. Sensitivity is the proportion of true positive cases belonging to the at-risk group based on the LPA (the "gold standard" group in this case). Specificity is the proportion of the true negatives among those who do not belong to the at-risk group based on the LPA [ 55, 56]. PPV is the proportion of true positives among all participants who scored positive on the test. NPV was defined as the proportion of true negatives among all participants with negative test results [ 56, 57]. Finally, accuracy measures the proportions of true negatives and true positives among all participants [ 56, 57].

All analyses were conducted on the weighted sample. Missing data were treated with Full-information maximum likelihood (FIML) method [ 49]. Statistical analyses were carried out with Mplus 7.3 [ 49] and IBM SPSS Statistics for Windows, Version 22.0 [ 58].

Results

Descriptive statistics

The final sample only comprised those participants who reported using the internet and social media (n = 5,961, 89.45% of the total sample). Approximately half (49.17%) of the sample was male (n = 2931). Age ranged between 15 and 22 years (mean age 16.60; SD = 0.94). The mean number of hours using social media was 23.16 hours per week (SD = 15.57). There was a significant difference in weekly social media use between male and female adolescents (mean time male = 20.53 hours, SD male = 15.71; mean time female = 25.71 hours, SD female = 15.00; U = 3672101, p< 0.001; r = -0.17).

Confirmatory factor analysis

A one-factor model with the six components (salience, tolerance, mood modification, relapse, withdrawal, conflict) as indicator variables was tested with confirmatory factor analysis. The analysis provided an acceptable fit to the data (χ ² = 5836.190 df = 15 p< 0.001; CFI = 0.950; TLI = 0.917; RMSEA = 0.073 (0.066-0.080) Cfit>0.90; SRMR = 0.034). All factor loadings were above the recommended threshold (>.50) and ranged from .598 to .814.

Latent profile analysis

The latent profile analysis was performed on the six items of the BSMAS, and according to the criteria, the three-class solution was selected as the best-fitting model (see Table 1). The AIC, BIC, and SSABIC values decreased continuously as more classes were added to the analysis. However, the scale of decrease somewhat diminished after the third latent class was added. Based on the L-M-R test, the three-class solution was accepted. The entropy of the two-class solution was the highest, but the entropy of the three-class solution was also adequate.

The features of the three classes are presented in Fig 1 and Table 2. The first class named 'no-risk' class represents the majority of social media users (78.3% of social media users; 70.7% of the total sample) who had the lowest scores on the BSMAS. The second class of social media users represents 'low risk' of problematic use (17.2% and 15.5% respectively), while the third class represents the population of 'at-risk' problematic social media users (4.5% and 4.1%, respectively). In the 'at-risk' group, 'withdrawal' and 'tolerance' criteria showed elevated levels compared to the other dimensions. Members of this class (i.e., those at-risk of problematic use) were likely to (a) be female, (b) use the internet and social media for more than 30 hours per week, (c) have lower self-esteem and higher level of depressive symptoms than social media users of the other two classes ( Table 2).

Suggesting a cutoff score for classification: Sensitivity and specificity analysis

Because of the lack of a clinically diagnosed group of problematic social media users, the third LPA class (i.e., those at-risk of problematic social media use) was used as the 'gold standard' to determine the optimal cut-off threshold to classify those at-risk of problematic use. Sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy of the BSMAS at all possible cut-off points were calculated ( Table 3). Based on this analysis, a cut-off score of 19 points was suggested as the ideal threshold at and above which individuals are classified as at-risk of problematic social media use.

In this case, the specificity is 99% and the sensitivity is 83% (i.e., only 1% of the non-problematic social media users are identified incorrectly as being at-risk of problematic use by the scale, while 17% of true cases of problematic social media users are missed). At this value, PPV is 73% and the NPV is 99%. In other words, 27% of the individuals with a positive test result are identified incorrectly, while only 1% of individuals with negative test result are identified incorrectly. This yields an accuracy of 98%. Increasing the cut-off point would result in more false negative cases, while decreasing it would increase the number of social media users labeled incorrectly (as being at-risk) by the screening instrument.

Discussion

To assess the prevalence of problematic social media use in a reliable and a valid way, the psychometric properties of the BSMAS were tested. According to the results, the BSMAS demonstrated adequate psychometric properties regarding its factor structure, reliability, and validity.

Using a latent profile analysis on the six items of the Bergen Social Media Addiction Scale (BSMAS), the adolescent social media users were divided into three different classes, and the analysis demonstrated that 4.5% of participants could be classified as being at-risk. Previous studies have shown a wide range of prevalence rates due to various methodological issues such as convenience sampling, targeting mainly college students, and/ or having small sample sizes [ 15, 12, 26, 42, 43]. For instance, the prevalence of problematic social media users among Nigerian University undergraduates was 1.6% [ 37], whereas among Malaysian college students the reported prevalence was 47% [ 43]. The results of the present study belong to the more conservative prevalence estimations. Moreover, they correspond to the prevalence rates of the general problematic and/or addictive Internet use, that range between 1% [ 59] and 18.7% [ 60] according to the recent review [ 61].

Regarding validity of the BSMAS, the at-risk group showed the lowest self-esteem and the highest level of depressive symptoms and the most time spent on internet and social media use, and was therefore in line with previous research findings [ 22, 24].

In addition, adolescents that were at-risk of social media use were mainly female, and reported the greatest amount of internet and social media usage. Previous studies have found similar gender differences in problematic social media use [ 4, 6] and problematic internet use [ 61]. For instance, Rehbein and Mößle [ 62] found that among adolescents with problematic internet use, girls indicated that social media contributed most to their addiction, while boys also cited online pornography as a primary source of their problems.

Furthermore, the results of the present study showed that within the at-risk group the withdrawal component had the highest score. Therefore, withdrawal symptoms should be highlighted when developing prevention and treatment programs in school environments for adolescents being at-risk of problematic social media use. A cut-off point was calculated using the third LPA class as the "gold standard" to categorize the risky or problematic social media users among adolescents in the sample. The suggested cut-off value with the most adequate sensitivity and specificity values was 19 points. Although, the calculated score cannot replace a clinically validated cut-off point, it may be more beneficial than using completely ad-hoc thresholds [ 63].

Despite the study's strengths (most notably the large nationally representative sample using psychometrically validated instruments), the study is not without its limitations. The study only included Hungarian adolescents as participants. Therefore, to further test the psychometric properties of the BSMAS, cross-cultural studies should also be conducted in the future using different adolescent groups in different countries and cultures. Moreover, the data were all self-report and self-report measures may lead to different response biases [ 64], such as social desirability bias (e.g., reporting more favorable behavior than the truth), memory recall bias (difficulty in remembering past events), and response style bias (e.g., scores may show central tendency or extreme response style).

It is also important to highlight that psychometric screening tools tend to overestimate the prevalence rates of disorders when the true prevalence rate of the disorder is low. For instance, an instrument with moderate sensitivity and specificity (i.e., 80.5% and 82.4%, respectively) at a 2.1% prevalence level of the problematic behavior has a positive predictive value of 8.9%. This means that out of 100 respondents who score positive on the test only about 9 are true clinical cases [ 65]. Consequently, survey-based prevalence rates should be interpreted cautiously to avoid overpathologizing every day behaviors [ 66]. More generally, the issue of addiction to social networking and social media is a controversial issue and many papers have questioned whether the activity can be considered an addiction at all [ 4, 27, 66- 68].

The study had a cross-sectional design, therefore causality cannot be established regarding the risk factors. Future research should apply longitudinal designs to identify the contributing factors of the problematic behavior among social media users. It should also be noted that when completing the BSMAS, the participants may have had a different conception of social media use than intended by the developers of the BSMAS. For instance, on sites such as Facebook, many different activities can be carried out such as social networking, gaming and gambling. Although the BSMAS is only concerned with social networking, there is always the possibility that participants' conception of social media use included some or all of these other activities and therefore problematic use might be including non-social networking activities.

Conclusion

In conclusion, the results of the present study suggest that the Bergen Social Media Addiction Scale [ 11] is a psychometrically valid scale that is an appropriate tool to identify the signs of risky social media use among adolescents. This instrument may be especially useful in school environments to identify those adolescents who are at-risk of problematic social media use and therefore could be utilized in prevention and intervention programs (i.e., content-control software, counseling, cognitive-behavioral therapy; [ 69]).

Acknowledgments

This study was supported by the Hungarian National Research, Development and Innovation Office (Grant numbers: K111938, K111740). Ágnes Zsila was supported by the New National Excellence Program awarded by the Ministry of Human Resources.

The funding institutions had no role in the study design or the collection, analysis and interpretation of the data, writing the manuscript, or the decision to submit the paper for publication.

Author Contributions

1. Conceptualization: ZD MDG OK.

2. Data curation: ZE FB ÁZ AM OK.

3. Formal analysis: FB ÁZ OK AM.

4. Funding acquisition: ZE ZD.

5. Investigation: ZE ZD AM.

6. Methodology: ZE ZD AM CSA OK.

7. Project administration: ZE ZD.

8. Resources: ZD ZE.

9. Software: ZE ZD.

10. Supervision: ZE ZD.

11. Validation: ZE ZD.

12. Visualization: ZD.

13. Writing - original draft: FB ÁZ OK AM.

14. Writing - review & editing: MDG CSA ZD ZE.

References

1. 25. Association AP. Diagnostic and Statistical Manual of Mental Disorders (DSM-5®). Fifth ed. Arlington, VA: American Psychiatric Association; 2013.

2. 67. Carbonell X, Panova T. A critical consideration of social networking sites' addiction potential. Addiction Research and Theory. 2016 Epub ahead of print. http://doi.org/10.1080/16066359.2016.1197915

3. 68. Ryan T, Chester A, Reece J, Xenos S. The uses and abuses of Facebook: A review of Facebook addiction. Journal of Behavioral Addictions. 2014; 3(3): 133-148. pmid:25317337

# data[].attributes.attachment_link.title
Problematic Social Media Use: Results from a Large-Scale Nationally Representative Adolescent Sample

# data[].attributes.attachment_link.description
Despite social media use being one of the most popular activities among adolescents, prevalence estimates among teenage samples of social media (problematic) use are lacking in the field. The present study surveyed a nationally representative Hungarian sample comprising 5,961 adolescents as part of the European School Survey Project on Alcohol and Other Drugs (ESPAD).

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A systematic review: the influence of social media on depression, anxiety and psychological distress in adolescents

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The purpose of this article was to gain a deeper understanding of how the use of social media affects the mental health of teenagers. The researchers employed a systematic review method and evaluated 13 different studies, most of which included qualitative questions that asked participants to respond about their social media habits and emotional well-being. Unfortunately, the results showed that the more time the participants spent on social media, the worse their mental health suffered specifically with depression and anxiety.

Betul Keles, Niall McCrae & Annmarie Grealish (2020) A systematic review: the influence of social media on depression, anxiety and psychological distress in adolescents, International Journal of Adolescence and Youth, 25:1, 79-93, DOI: 10.1080/02673843.2019.1590851

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ABSTRACT

While becoming inextricable to our daily lives, online social media are blamed for increasing mental health problems in younger people. This systematic review synthesized evidence on the influence of social media use on depression, anxiety and psychological distress in adolescents. A search of PsycINFO, Medline, Embase, CINAHL and SSCI databases reaped 13 eligible studies, of which 12 were cross-sectional. Findings were classified into four domains of social media: time spent, activity, investment and addiction. All domains correlated with depression, anxiety and psychological distress. However, there are considerable caveats due to methodological limitations of cross-sectional design, sampling and measures. Mechanisms of the putative effects of social media on mental health should be explored further through qualitative enquiry and longitudinal cohort studies.

Introduction

Children and adolescent mental health

Reasons for the apparently growing psychological morbidity in young people are not known conclusively. McCrae ( Citation2018) suggests that diagnostic activity has been influenced by educational initiatives to raise mental health awareness. Undeterred by stigma, many young people feel free to discuss their psychological difficulties and seek professional help. Another important factor is the ease of sharing personal experiences in the digital information age (Reid-Chassiakos, Radesky, Christakis, & Moreno, Citation2016). Whereas in the past mental health problems were suffered in isolation, today a struggling younger person can readily find others with similar problems, either through social interaction or support groups. Alongside increasing awareness and help-seeking behaviour, doctors may be more inclined to diagnose and treat mental health problems, possibly with the effect of lowering the diagnostic threshold.

Social media

The term 'social media' refers to the various internet-based networks that enable users to interact with others, verbally and visually (Carr & Hayes, Citation2015). According to the Pew Research Centre ( Citation2015), at least 92% of teenagers are active on social media. Lenhart, Smith, Anderson, Duggan, and Perrin ( Citation2015) identified the 13-17 age group as particularly heavy users of social media users, with 87% having access to a computer, and 58% to a tablet device. Almost three-quarters of adolescents aged 15 to17 use a smartphone, and 68% of those aged 13 to 14 (Pew Research Centre, Citation2015).

Impact on mental health

Understanding the impact of social media on adolescents' well-being has become a priority due to a simultaneous increase in mental health problems (Kim, Citation2017). Problematic behaviours related to internet use are often described in psychiatric terminology, such as 'addiction'. However, some activity in younger people could be misconstrued as abnormal. For example, young people who frequently post images of themselves ('selfies') may appear narcissistic, but such behaviour has emerged as a social norm in younger social networks (McCrae, Citation2018). Nonetheless, warnings have been issued by psychologists and other experts on how younger people are engaging with social media and related impairment to personal and social development (Greenfield, Citation2014; Twenge, Citation2006).

Current study

This systematic review examined evidence for the influence of social media use on depression, anxiety and psychological distress in adolescents. The intention was to inform policy and practice and to indicate further research on this topic.

Method

Protocol and registration

For transparency, the protocol for this review was registered with the International Prospective Register of Systematic Reviews (Prospero; CRD42018102770). This report follows the guidelines of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement (Moher, Liberati, Tetzlaff, & Altman, Citation2009a).

Eligibility criteria

For inclusion in this review, studies fulfilled the following eligibility criteria:

Participants: aged 13 to 18

Exposure: measurement of social media use

Outcome: depression, anxiety or psychological distress, assessed by validated instruments

Studies published in peer-reviewed journals with full text available in English

Studies were excluded if they crossed either boundary of the age range. Studies measuring exposure to other internet activities such as video-gaming were not included unless social media use was also measured. Outcomes of substance misuse, eating disorder, well-being, life satisfaction, self-esteem, body image problems, conduct disorders, loneliness or stress were excluded, unless the outcomes of interest were also measured by the researchers.

Search strategy

The databases Medline, Embase, PsychINFO, Cumulative Index to Nursing and Allied Health (CINAHL) and Social Sciences Citation Index (SSCI) were systematically searched in May 2018. A set of search terms was created with truncations, Medical Subject Headings (MESH) and Boolean operators, as shown in Table 1.

Data extraction

All papers from the automated database searches were collated using the Mendeley reference management software. After duplicates were deleted, screening was conducted to ensure that studies fulfilled the eligibility criteria. In a three-stage process, papers were screened on title and on abstract (by BK) and the remaining papers were screened on full text (by BK, NM and AG). Key information relevant to the research question was systematically extracted and tabulated to aid comparison and synthesis of the studies. These data comprised authors, publication date, country of origin, study design and data analysis method, relevant outcome measures, sample size, demographic data and results. The extraction process was conducted by BK and AG and any disagreements resolved through discussion with NM.

Assessment of quality

The quality of eligible studies was assessed using the National Institutes of Health Quality Assessment tool for Observational Cohort and Cross-Sectional Studies (NIH, Citation2014), which covers design, selection bias, data collection, confounders, blinding and attrition. An overall rating of 'good', 'fair' or 'poor' was given for each study. All of the studies were independently rated by BK and AG, and any disagreements were resolved through discussion with NM.

Data analysis

As outcome measures varied across the studies, we were unable to perform meta-analysis. Instead, narrative synthesis was conducted. This enabled consideration of confounding, mediating and moderating variables, which are often not given due attention in meta-analysis (Popay et al., Citation1995). Each study was described, followed by comparative analysis and synthesis.

Results

The literature search yielded 6598 articles from the five databases. After 1818 duplicates were removed, screening on title excluded 4206 of the 4780 unique papers. The remaining 574 articles were screened on abstract, with 475 removed, leaving 99 papers. On reading the full text, 86 papers found to be ineligible, the most common reason being the age range. The PRISMA (Moher, Liberati, Tetzlaff, & Altman; The PRISMA Group, Citation2009b) flowchart ( Figure 1) provides further detail on reasons for exclusion. Ultimately a total of 13 papers were eligible for the review.

Description of studies

The reviewed papers are summarized in Table 2. In design, 12 studies were cross-sectional and only one was longitudinal. The total sample across the studies was 21,231. Three studies were conducted in Australia, three in China, and one each in Serbia, USA, Malaysia, Belgium, Thailand and Canada; one study was conducted in six European counties including Greece, Spain, Poland, the Netherlands, Romania and Iceland. Participant ages ranged from 13 to 18. Nine studies covered exposure to social media in general, while four studies (Banjanin, Banjanin, Dimitrijevic, & Pantic, Citation2015; Dumitrache, Mitrofan, & Petrov, Citation2012; Frison & Eggermont, Citation2016; Hanprathet, Manwong, Khumsri, Yingyeun, & Phanasathit, Citation2015) focused specifically on Facebook use. As well as measuring depression, anxiety or psychological distress, some studies investigated confounding variables (e.g. age and gender) and mediating and moderating factors (e.g. insomnia, rumination and self-esteem). Ten of the studies entailed both correlational and regression analyses; one study (Dumitrache et al., Citation2012) performed correlational analyses and a t-test for gender differences; Vernon, Modecki, and Barber ( Citation2017) used latent growth modelling to test longitudinal mediation.

Quality assessment

The aim was clearly stated for almost all of the studies, but generally, the methodological quality was poor to fair ( Table 3). The cross-sectional design of 12 studies is susceptible to three common sources of bias: selection, information and confounding bias (Yu & Tse, Citation2012). Two studies (Barry et al., Citation2017; Wang et al., Citation2018) recruited by convenience sampling, which raises the risk of selection bias. O'Dea and Campbell ( Citation2011) omitted their sampling procedure. Apart from Hanprathet et al. ( Citation2015), the papers did not state or explain their intended sample size, of which two were small (Barry et al., Citation2017; Dumitrache et al., Citation2012). Five studies did not report the participant response rate. Barry et al. ( Citation2017) had a response rate of 33%, which increases the risk of bias and limits generalizability of the results.

Analysis of results

Key findings of the studies were classified into four common domains of exposure to social media: time spent, activity, investment and addiction. Time spent refers to the amount of time that users spent on social media. Activity can be defined as the quality and quantity of users' engagement and interaction with social media sites and other users. Investment refers to the act of putting effort and time into social media whereas addiction refers to the state of being dependent on social media. For each domain we discuss the relationship with depression, anxiety and psychological distress, with reference to confounding, mediating or moderating variables if measured.

Time spent

The studies produced opposing evidence on the relationship between time spent on social media and mental health problems. With an Australian sample, O'Dea and Campbell ( Citation2011) found an inverse correlation for psychological distress; no relationship between frequency of social media use and depressed mood was reported by Neira and Barber ( Citation2014) in another study in Australia, and Banjanin et al. ( Citation2015) in Serbia. By contrast, Sampasa-Kanyinga and Lewis ( Citation2015) in Canada found that daily social media use of over two hours was associated with psychological distress. A study of 10,930 adolescents from six European countries by Tsitsika et al. ( Citation2014) showed a positive relationship between heavy social media use and both depression and anxiety. Yan et al. ( Citation2017) found that time spent on social media was associated with anxiety in Chinese adolescents.

Activity

Frison and Eggermont ( Citation2016) found that both active and passive use of Facebook, in a sample of Belgian high school pupils, correlated with an increased frequency of depressed mood. In a study of 113 adolescent-parent dyads, Barry et al. ( Citation2017) found that data from parents showed correlation between adolescents' social media activities (i.e. number of accounts, frequency of checking for messages) and both anxiety and depression. However, Banjanin et al. ( Citation2015) did not find any relationship between social media activities (i.e. number of 'selfies') and depression in Serbian high school pupils.

Investment

Dumitrache et al. ( Citation2012) found a significant correlation between the number of identity-related information on Facebook profiles and depressive tendencies in adolescents. The studies by Neira and Barber ( Citation2014) and by Vernon et al. ( Citation2017), both using secondary data from the Youth Activity Participation Study of Western Australia, investigated the relationship between investment in social media and depressed mood. The cross-sectional study by Neira and Barber ( Citation2014) showed that investment in social media sites was associated with an increased depressed mood. Vernon et al. ( Citation2017) conducted a longitudinal investigation and found an association between problematic social media investment and depressed mood, with sleep disruption as a mediating variable.

Addiction

Three studies focused on addictive behaviour. Hanprathet et al. ( Citation2015) found a significant association between Facebook addiction and depression among 972 high school pupils in affluent districts in Thailand. A study of Chinese secondary school students by Li et al. ( Citation2017) showed a mediating influence of insomnia on the statistically significant relationship between social media addiction and depression. In another study in China, Wang et al. ( Citation2018) found that social networking sites addiction was positively associated with depression; rumination mediated the relationship between social networking sites addiction and depression while self-esteem moderated this mediating effect. In other words, low self-esteem compounded the impact of addiction on depression through rumination.

Confounding factors

Four studies measured the effect of gender in the relationship between social media-related variables and mental health outcomes. Neira and Barber ( Citation2014) found that social media might have negative aspects for female youth while being a positive leisure activity for male youth. Frison and Eggermont ( Citation2016) found that girls who passively use Facebook and boys who actively use Facebook in a public setting were more likely to be affected by the negative impacts of Facebook. Banjanin et al. ( Citation2015) did not find any significant effect of gender in the relationship between depression and time spent on social media. Similarly, Barry et al. ( Citation2017) did not find any change in the analysis when controlling for gender in the relationship between social media use and depression as well as between social media use and anxiety.

Two studies measured the effect of age. Tsitsika et al. ( Citation2014) found a significant effect of age in the relationship between heavy social media use and negative internalizing symptoms (anxious/depressed, withdrawn/depressed), with younger heavier social media users being more likely to experience internalizing symptoms compared to older heavier users. Banjanin et al. ( Citation2015) did not find any significant age effect in the relationship between depression and time spent on social media.

Discussion

This systematic review examined the evidence for a putative relationship between social media use and mental health problems in adolescents. In the 13 studies, depression was the most commonly measured outcome. The prominent risk factors for depression, anxiety and psychological distress emerging from this review comprised time spent on social media, activities such as repeated checking for messages, personal investment, and addictive or problematic use.

Although results of the studies were not entirely consistent, this review found a general correlation between social media use and mental health problems. However, most authors noted that the observed relationship is too complex for straightforward statements. Few studies were designed to explore this complexity although some assessed the effect of mediating and moderating factors. Insomnia and other sleep-related factors were most frequently reported as mediators of the relationship between social media use and depressed mood (Li et al., Citation2017; Vernon et al., Citation2017). Perceived social support (Frison & Eggermont, Citation2016) and rumination (Wang et al., Citation2018) were other mediating factors reported in the studies. Researchers suggested further investigation of these factors, and other factors such as personal traits (O'Dea & Campbell, Citation2011), socio-cultural factors that influence the roles of and expectations from adolescents in family and society, environmental factors which may affect development of adolescents and social skills (Tsitsika et al., Citation2014), motivations for social media use (Barry et al., Citation2017; O'Dea & Campbell, Citation2011), social comparison and peer feedback (Neira & Barber, Citation2014), self-esteem (Banjanin et al., Citation2015), contextual factors, lack of physical activity, and cyberbullying (Sampasa-Kanyinga & Lewis, Citation2015).

Other important findings of this review suggest that particular attitudes or behaviours (e.g. social comparison, active or passive use of social media, motives for social media use) may have a greater influence on the symptoms of depression, anxiety and psychological distress than the frequency of social media use or the number of online friends. Although there is evidence of a relationship between time spent on social media and depression as well as social media-related activities and depression, contrary findings have also emerged. For example, Banjanin et al. ( Citation2015) found no relationship between the amount of time spent on social media and depression, or between social media-related activities such as the number of online friends and the number of 'selfies' and depression. Similarly, Neira and Barber ( Citation2014) found that while higher investment in social media (e.g. active social media use) predicted adolescents' depressive symptoms, no relationship was found between the frequency of social media use and depressed mood. Such mixed findings might be explained by confounders, mediators and moderators as discussed above.

This systematic review also sheds light on the influence of age and sex. Although some studies found that these variables had no effect on the relationship between social media use and mental health problems, other studies showed that girls and younger adolescents are more prone to depression and anxiety. Further investigation is needed to assess the effects of age and gender.

Limitations

Although the results of this systematic review contributed to the existing literature in a way of providing considerable evidence for the mental health impact of social media use by focussing on not only the symptoms of depression but also other related outcomes including anxiety and psychological distress among adolescents who are at higher risk of developing anxiety and depression. Several limitations in the evidence emerged from included studies and review process have been identified. First, 12 out of 13 studies did not answer the review question since they were cross-sectional and unable to determine a causal relationship between the variables of interest. Looking evidence emerged from cross-sectional studies, it is not possible to decide whether social media use causes depression, anxiety and psychological distress, or whether those with depression, anxiety and psychological distress are more likely to spend more time on social media; have addictive and problematic social media use behaviour; have negative interaction with social media; and invest on social media. Only one longitudinal study (Vernon et al., Citation2017) investigated the causal relationship between problematic social media use and change in depressed mood, but this study has also limited to show evidence whether social media use causes depressed mood in adolescents. The study did not use a control and a comparison group to differentiate those who exposed to social media sites and those who not. Therefore, it is difficult to determine whether a change in depressed mood was more in those who exposed to social media more compared to those who less or not.

Second, small sample size and the use of convenience sampling in some studies limited the representativeness of and generalizability to a larger adolescent population. Third, all studies included in this review used self-report measures which may not provide reliable outcomes because of some sources of risk of bias. Participants may show positive self-presentation by over- or under-reporting their social media-related behaviours and some mental health-related items, which may directly or indirectly lead to social desirability bias, information bias and reporting bias. Another identified limitation was that some studies made an investigation towards only Facebook use over other social media sites, which also causes a significant bias and limits the generalizability of findings to other social media sites. Finally, despite the fact that the proposed relationship between social media-related variables, depression, anxiety and psychological distress is complex, few studies investigated mediating factors that may contribute or exacerbate this relationship. Further investigations are needed to explain the underlying factors that help determine why social media has negative impact on some adolescents' mental health whereas it has no or positive affect on others' mental health.

Conclusion

The impact of social media use on incidence of depression, anxiety and psychological distress among adolescents, as examined by this review, is likely to be multifactorial. It is important to distinguish between the terms used for the relationship. It is fair to say that there is an 'association' between social media use and mental health problems, on the basis that this means a socially constructed reality. But this is not necessarily scientifically valid. Objective researchers investigate correlations rather than accepting socially assumed truths. Correlation is statistical, not phenomenal. Thirdly, there is causation, which requires directional evidence. The latter has not been adequately investigated in this topic, and we must, therefore, state that the relationship is correlational but not conclusively causative.

Key findings of included studies were classified into four categories of exposure to social media: time spent; activity; investment; and addiction. All these categories were found as correlated with depression, anxiety and psychological distress, with an acknowledgement for the complexity of these relationships. Although there are studies which investigated mediating and moderating factors that may contribute or exacerbate the proposed relationship, there are still several underexplored mediators and moderators, which may explain the direction of this relationship. We also identified gaps in literature in terms of methods, study design and sampling. Causality was unclear due to the cross-sectional study design used in almost all studies and the lack of comparison group in the cohort study. Also, the number of quantitative studies in literature is substantially higher than qualitative studies. Through this systematic review, we hope we contribute to the existing literature in the way of addressing the gaps and highlighting the importance of the phenomenon of the mental health impact of social media use on adolescents.

Acknowledgments

The authors would like to thank Dr Sorina Daniela Dumitrache who supplied us the full-text of their study which was not available online. The authors also declare that there is no conflict of interest regarding the publication of this article.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Notes on contributors

Betul Keles

Niall McCrae

Annmarie Grealish

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A systematic review: the influence of social media on depression, anxiety and psychological distress in adolescents

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While becoming inextricable to our daily lives, online social media are blamed for increasing mental health problems in younger people. This systematic review synthesized evidence on the influence ...

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The purpose of this article is to evaluate the impact of online communication, specifically through social media, on the mental health of adolescents using a systematic narrative review. Some research highlighted the positive effects of online communication by examining how teens felt more connected and supported through the use of social media. On the other hand, the data also shows that teens feel their issues with anxiety and depression are linked to cyberbullying and social media.

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Widman, L., Javidi, H., Maheux, A. J., Evans, R., Nesi, J., & Choukas-Bradley, S. (2021). Sexual communication in the digital age: Adolescent sexual communication with parents and friends about sexting, pornography, and starting relationships online. Sexuality & Culture, 25(6), 2092–2109. https://doi.org/10.1007/s12119-021-09866-1

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The focus of this article is the rising concern of cyberbullying and sexting, and how it pertains to teens and their technology. This study focused on middle school students, and the findings showed that a substantial number of students had been involved in digital harassment but didn't report it for various reasons. The article also addresses the need for schools to create policies that address sexting and online safety, as well as educate all students about digital consent and boundaries, and the consequences for not respecting them.

Siegle, D. (2010). Cyberbullying and sexting: Technology abuses of the 21st century. Gifted Child Today, 33(2), 14–17. https://files.eric.ed.gov/fulltext/EJ881324.pdf

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The purpose of this article is to look at the impact that sexting has in schools, both on the discipline side and the counseling side. The research and cases highlight the need for school-wide policies regarding digital boundaries and how to make informed decisions. It also showcases the need for schools to educate students about the legal consequences of sexting as well as the emotional toll.

Van Ouytsel, J., Walrave, M., & Ponnet, K. (2012). Sexting: New challenges for schools and professional school counselors. Professional School Counseling, 16(1), 1–9. https://eric.ed.gov/?id=EJ981201

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The purpose of this article is to look at how modern vaping devices give higher doses of nicotine than a regular cigarette, as well as the nicotine industry emphasizes the role of flavored products in increasing youth appeal and the risk of addiction due to frequent use. The article also discusses some of the substances found in vapes, including aldehydes, heavy metals, and diacetyl, which are commonly present in vape liquids.

Winickoff, J. P., Evins, A. E., & Levy, S. (2024). Vaping in youth. JAMA, 332(9). https://jamanetwork.com/journals/jama/fullarticle/2822166

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This study utilized data from the National Youth Tobacco Survey (NYTS) and the Youth Risk Behavior Surveillance System (YRBSS) from 2014 to 2021, focusing on trends in e-cigarette use and dependence over time. This was a survey-based longitudinal study that found the baseline of teens using e-cigarettes within five minutes of waking up as an indicator of addiction.

Glantz, S., Jeffers, A., & Winickoff, J. P. (2022). Nicotine addiction and intensity of e-cigarette use by adolescents in the US, 2014 to 2021. JAMA Network Open, 5(11). https://jamanetwork.com/journals/jamanetworkopen/fullarticle/2798130

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 Trends in Use and Perceptions of Nicotine Vaping Among US Youth From 2017 to 2020

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Miech, R., Leventhal, A., Johnston, L., O’Malley, P. M., Patrick, M. E., & Barrington-Trimis, J. (2021). Trends in use and perceptions of nicotine vaping among US youth from 2017 to 2020. JAMA Pediatrics, 175(2). https://jamanetwork.com/journals/jamapediatrics/fullarticle/2774132

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