More than screen deep - finding the inflection point of problematic social media use
DOI:
https://doi.org/10.29329/jsomer.112Keywords:
problematic social media use, Social media addiction , Bergen Social Media Addiction Scale (BSMAS), Social media engagement, Addictive pullAbstract
Most empirical applications of the Bergen Social Media Addiction Scale (BSMAS) rely on student or convenience samples and often treat screentime as the main behavioral proxy. This article addresses both limitations using a representative CAWI survey of 2,033 adults in Germany conducted in May 2025. Building on clinically informed BSMAS groupings, we examine the population distribution of problematic use, the suitability of a generalized conflict item for respondents outside work or study, the usefulness of a single-item measure of platform-specific addictive pull across 18 social media services, and the comparative value of engagement-based versus frequency-based indicators. Problematic use appeared to be linked to age. Only 2.2% of respondents fell into the addicted category, but 12.3% were classified as at-risk, with markedly higher shares among younger cohorts. The amended BSMAS item for respondents not working or studying showed comparable psychometric performance to the standard wording, preserving reliability, inter-item correlation, and excellent one-factor model fit. The single-item measure of addictive pull showed a moderate positive association with the global BSMAS and allows parsimonious within-subject comparisons across platforms, although it should not replace the full scale. Most importantly, posting- and reaction-related engagement were more strongly associated with BSMAS than service intensity and, unlike intensity, significantly distinguished low-risk from at-risk users. The findings suggest that problematic social media use in the adult population is neither ubiquitous nor well captured by screentime alone. For research, prevention, and platform governance, engagement dynamics and social feedback appear to provide a more informative basis for identifying users approaching higher risk than exposure-based measures alone.
References
Andreassen, C. S., Billieux, J., Griffiths, M. D., Kuss, D. J., Demetrovics, Z., Mazzoni, E., & Pallesen, S. (2016). The relationship between addictive use of social media and video games and symptoms of psychiatric disorders: A large-scale cross-sectional study. Psychology of Addictive Behaviors, 30(2), 252–262. https://doi.org/10.1037/adb0000160
Andreassen, C. S., & Pallesen, S. (2014). Social network site addiction—an overview. Current Pharmaceutical Design, 20(25), 4053–4061. https://doi.org/10.2174/13816128113199990616
Andreassen, C. S., Pallesen, S., & Griffiths, M. D. (2017). The relationship between addictive use of social media, narcissism, and self-esteem: Findings from a large national survey. Addictive Behaviors, 64, 287–293. https://doi.org/10.1016/j.addbeh.2016.03.006
Andreassen, C. S., Torsheim, T., Brunborg, G. S., & Pallesen, S. (2012). Development of a Facebook Addiction Scale. Psychological Reports, 110(2), 501–517. https://doi.org/10.2466/02.09.18.PR0.110.2.501-517
American Psychiatric Association. (2022). Diagnostic and statistical manual of mental disorders (5th ed., text rev.). https://doi.org/10.1176/appi.books.9780890425787
Arnold, R., & Waldburger, M. (2015). The economic influence of data and their impact on business models. In Trends in telecommunication reform 2015: Getting ready for the digital economy (pp. 153–183). International Telecommunication Union. http://handle.itu.int/11.1002/pub/80c5340e-c5bacb09-en
Bányai, F., Zsila, Á., Király, O., Maraz, A., Elekes, Z., Griffiths, M. D., Andreassen, C. S., & Demetrovics, Z. (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
Bayer, J. B., Campbell, S. W., & Ling, R. (2016). Connection cues: Activating the norms and habits of social connectedness. Communication Theory, 26(2), 128–149. https://doi.org/10.1111/comt.12090
Bottaro, R., Griffiths, M. D., & Faraci, P. (2026). Meta-analysis of reliability and validity of the Bergen Social Media Addiction Scale (BSMAS). International Journal of Mental Health and Addiction, 24(2), 1438–1465. https://doi.org/10.1007/s11469-025-01461-x
Brailovskaia, J., & Margraf, J. (2024). Addictive social media use during Covid-19 outbreak: Validation of the Bergen Social Media Addiction Scale (BSMAS) and investigation of protective factors in nine countries. Current Psychology, 43(14), 13022–13040. https://doi.org/10.1007/s12144-022-03182-z
Bunker, C. J., & Kwan, V. S. Y. (2021). Do the offline and social media Big Five have the same dimensional structure, mean levels, and predictive validity of social media outcomes? Cyberpsychology: Journal of Psychosocial Research on Cyberspace, 15(4), Article 8. https://doi.org/10.5817/CP2021-4-8
Casale, S., & Fioravanti, G. (2015). Satisfying needs through social networking sites: A pathway towards problematic Internet use for socially anxious people? Addictive Behaviors Reports, 1, 34–39. https://doi.org/10.1016/j.abrep.2015.03.008
Castro-Calvo, J., King, D. L., Stein, D. J., Brand, M., Carmi, L., Chamberlain, S. R., Demetrovics, Z., Fineberg, N. A., Rumpf, H.-J., Yücel, M., Achab, S., Ambekar, A., Bahar, N., Blaszczynski, A., Bowden-Jones, H., Carbonell, X., Chan, E. M. L., Ko, C.-H., de Timary, P., . . . Billieux, J. (2021). Expert appraisal of criteria for assessing gaming disorder: An international Delphi study. Addiction, 116(9), 2463–2475. https://doi.org/10.1111/add.15411
Chen, I.-H., Ahorsu, D. K., Pakpour, A. H., Griffiths, M. D., Lin, C.-Y., & Chen, C.-Y. (2020). Psychometric properties of three simplified Chinese online-related addictive behavior instruments among mainland Chinese primary school students. Frontiers in Psychiatry, 11, 875. https://doi.org/10.3389/fpsyt.2020.00875
Chen, I.-H., Strong, C., Lin, Y.-C., Tsai, M.-C., Leung, H., Lin, C.-Y., Pakpour, A. H., & Griffiths, M. D. (2020). Time invariance of three ultra-brief internet-related instruments: Smartphone Application-Based Addiction Scale (SABAS), Bergen Social Media Addiction Scale (BSMAS), and the Nine-Item Internet Gaming Disorder Scale–Short Form (IGDS-SF9) (study Part B). Addictive Behaviors, 101, 105960. https://doi.org/10.1016/j.addbeh.2019.04.018
Cheng, C., Ebrahimi, O. V., & Luk, J. W. (2022). Heterogeneity of prevalence of social media addiction across multiple classification schemes: Latent profile analysis. Journal of Medical Internet Research, 24(1), e27000. https://doi.org/10.2196/27000
Copez-Lonzoy, A., Vallejos-Flores, M., Capa-Luque, W., Salas-Blas, E., Montero Doig, A. M., Dias, P. C., & Bazo-Alvarez, J. C. (2023). Adaptation of the Bergen Social Media Addiction Scale (BSMAS) in Spanish. Acta Psychologica, 241, 104072. https://doi.org/10.1016/j.actpsy.2023.104072
Dadiotis, A., Bacopoulou, F., Kokka, I., Vlachakis, D., Chrousos, G. P., Darviri, C., & Roussos, P. (2021). Validation of the Greek version of the Bergen Social Media Addiction Scale in undergraduate students. EMBnet.journal, 26, e975. https://doi.org/10.14806/ej.26.1.975
Dimock, M. (2019, January 17). Defining generations: Where Millennials end and Generation Z begins. Pew Research Center. https://www.pewresearch.org/short-reads/2019/01/17/where-millennials-end-and-generation-z-begins/
Funke, F., & Reips, U.-D. (2012). Why semantic differentials in web-based research should be made from visual analogue scales and not from 5-point scales. Field Methods, 24(3), 310–327. https://doi.org/10.1177/1525822X12444061
Gaver, W. W. (1991). Technology affordances. In S. P. Robertson, G. M. Olson, & J. S. Olson (Eds.), Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (pp. 79–84). Association for Computing Machinery. https://doi.org/10.1145/108844.108856
Gaver, W. W. (1992). The affordances of media spaces for collaboration. In J. Turner & R. Kraut (Eds.), Proceedings of the 1992 ACM Conference on Computer-Supported Cooperative Work (pp. 17–24). Association for Computing Machinery. https://doi.org/10.1145/143457.371596
Goldsmith, A., & Wall, D. S. (2022). The seductions of cybercrime: Adolescence and the thrills of digital transgression. European Journal of Criminology, 19(1), 98–117. https://doi.org/10.1177/1477370819887305
Griffiths, M. (2005). A ‘components’ model of addiction within a biopsychosocial framework. Journal of Substance Use, 10(4), 191–197. https://doi.org/10.1080/14659890500114359
Kim, H., Schlicht, R., Schardt, M., & Florack, A. (2021). The contributions of social comparison to social network site addiction. PLOS ONE, 16(10), e0257795. https://doi.org/10.1371/journal.pone.0257795
Lee, S. Y. (2014). How do people compare themselves with others on social network sites?: The case of Facebook. Computers in Human Behavior, 32, 253–260. https://doi.org/10.1016/j.chb.2013.12.009
Lemola, S., Perkinson-Gloor, N., Brand, S., Dewald-Kaufmann, J. F., & Grob, A. (2015). Adolescents’ electronic media use at night, sleep disturbance, and depressive symptoms in the smartphone age. Journal of Youth and Adolescence, 44(2), 405–418. https://doi.org/10.1007/s10964-014-0176-x
Leung, H., Pakpour, A. H., Strong, C., Lin, Y.-C., Tsai, M.-C., Griffiths, M. D., Lin, C.-Y., & Chen, I.-H. (2020). Measurement invariance across young adults from Hong Kong and Taiwan among three internet-related addiction scales: Bergen Social Media Addiction Scale (BSMAS), Smartphone Application-Based Addiction Scale (SABAS), and Internet Gaming Disorder Scale–Short Form (IGDS-SF9) (study Part A). Addictive Behaviors, 101, 105969. https://doi.org/10.1016/j.addbeh.2019.04.027
Licoppe, C. (2004). ‘Connected’ presence: The emergence of a new repertoire for managing social relationships in a changing communication technoscape. Environment and Planning D: Society and Space, 22(1), 135–156. https://doi.org/10.1068/d323t
Lin, L. Y., Sidani, J. E., Shensa, A., Radovic, A., Miller, E., Colditz, J. B., Hoffman, B. L., Giles, L. M., & Primack, B. A. (2016). Association between social media use and depression among U.S. young adults. Depression and Anxiety, 33(4), 323–331. https://doi.org/10.1002/da.22466
Ling, R. (2012). Taken for grantedness: The embedding of mobile communication into society. MIT Press. https://doi.org/10.7551/mitpress/8445.001.0001
Luo, T., Qin, L., Cheng, L., Wang, S., Zhu, Z., Xu, J., Chen, H., Liu, Q., Hu, M., Tong, J., Hao, W., Wei, B., & Liao, Y. (2021). Determination the cut-off point for the Bergen Social Media Addiction (BSMAS): Diagnostic contribution of the six criteria of the components model of addiction for social media disorder. Journal of Behavioral Addictions, 10(2), 281–290. https://doi.org/10.1556/2006.2021.00025
Ma, J.-L., Jin, Z., & Liu, C. (2025). The Bergen Facebook Addiction Scale: A reliability generalization meta-analysis. Frontiers in Psychology, 15, 1444039. https://doi.org/10.3389/fpsyg.2024.1444039
Machande, A. D., Smith, L. G. E., & Joinson, A. (2026). Development and testing of a dimensional typology of cyberdeviance. The Information Society, 42(1), 1–19. https://doi.org/10.1080/01972243.2025.2571087
Naher, L., Hiramoni, F. A., Alam, N., & Ahmed, O. (2022). Psychometric assessment of the Bangla version of the Bergen Social Media Addiction Scale. Heliyon, 8(7), e09929. https://doi.org/10.1016/j.heliyon.2022.e09929
Nesi, J., Telzer, E. H., & Prinstein, M. J. (2020). Adolescent development in the digital media context. Psychological Inquiry, 31(3), 229–234. https://doi.org/10.1080/1047840X.2020.1820219
Ozimek, P., & Bierhoff, H.-W. (2020). All my online-friends are better than me—three studies about ability-based comparative social media use, self-esteem, and depressive tendencies. Behaviour & Information Technology, 39(10), 1110–1123. https://doi.org/10.1080/0144929X.2019.1642385
Ozimek, P., Brailovskaia, J., & Bierhoff, H.-W. (2023). Active and passive behavior in social media: Validating the Social Media Activity Questionnaire (SMAQ). Telematics and Informatics Reports, 10, 100048. https://doi.org/10.1016/j.teler.2023.100048
Przybylski, A. K., Murayama, K., DeHaan, C. R., & Gladwell, V. (2013). Motivational, emotional, and behavioral correlates of fear of missing out. Computers in Human Behavior, 29(4), 1841–1848. https://doi.org/10.1016/j.chb.2013.02.014
Sahebi, S., & Formosa, P. (2022). Social media and its negative impacts on autonomy. Philosophy & Technology, 35(3), Article 70. https://doi.org/10.1007/s13347-022-00567-7
Santini, Z. I., Thygesen, L. C., Andersen, S., Tolstrup, J. S., Koyanagi, A., Nielsen, L., Meilstrup, C., Koushede, V., & Ekholm, O. (2025). Social media addiction predicts compromised mental health as well as perceived and objective social isolation in Denmark: A longitudinal analysis of a nationwide survey linked to register data. International Journal of Mental Health and Addiction, 23(4), 3136–3153. https://doi.org/10.1007/s11469-024-01283-3
Savolainen, I., Brailovskaia, J., Sirola, A., Celuch, M., & Oksanen, A. (2025). Just a few more minutes: Longitudinal and cross-national perspectives on the role of online identity bubbles in addictive internet use. Computers in Human Behavior, 165, 108555. https://doi.org/10.1016/j.chb.2025.108555
Schulze, J., Zagorscak, P., West, S. G., Schultze, M., & Krumm, S. (2022). Mind the context—The relevance of personality for face-to-face and computer-mediated communication. PLOS ONE, 17(8), e0272938. https://doi.org/10.1371/journal.pone.0272938
Sindermann, C., Elhai, J. D., & Montag, C. (2020). Predicting tendencies towards the disordered use of Facebook’s social media platforms: On the role of personality, impulsivity, and social anxiety. Psychiatry Research, 285, 112793. https://doi.org/10.1016/j.psychres.2020.112793
Stănculescu, E. (2023). The Bergen Social Media Addiction Scale validity in a Romanian sample using item response theory and network analysis. International Journal of Mental Health and Addiction, 21(4), 2475–2492. https://doi.org/10.1007/s11469-021-00732-7
Stănculescu, E., & Griffiths, M. D. (2022). Social media addiction profiles and their antecedents using latent profile analysis: The contribution of social anxiety, gender, and age. Telematics and Informatics, 74, 101879. https://doi.org/10.1016/j.tele.2022.101879
Steiger, J. H. (1980). Tests for comparing elements of a correlation matrix. Psychological Bulletin, 87(2), 245–251. https://doi.org/10.1037/0033-2909.87.2.245
Sun, Y., & Zhang, Y. (2021). A review of theories and models applied in studies of social media addiction and implications for future research. Addictive Behaviors, 114, 106699. https://doi.org/10.1016/j.addbeh.2020.106699
Taylor, K. (2024). The social diagnoses of digital addictions: Technophobic ambivalences, the limits of the natural and imperatives of self-governance in the information age. Sociology of Health & Illness, 46(S1), 18–36. https://doi.org/10.1111/1467-9566.13624
Torres-Pantoja, S., Rhee, L., Unkel, J., Saleem, M., & Bayer, J. B. (2026). Which platforms count? The diverse meanings of “social media” in the United States. Psychology of Popular Media. Advance online publication. https://doi.org/10.1037/ppm0000656
Twenge, J. M., & Campbell, W. K. (2019). Media use is linked to lower psychological well-being: Evidence from three datasets. Psychiatric Quarterly, 90(2), 311–331. https://doi.org/10.1007/s11126-019-09630-7
Verbeij, T., Pouwels, J. L., Beyens, I., & Valkenburg, P. M. (2022). Experience sampling self-reports of social media use have comparable predictive validity to digital trace measures. Scientific Reports, 12(1), 7611. https://doi.org/10.1038/s41598-022-11510-3
World Health Organization. (2022). International classification of diseases, eleventh revision (ICD-11). https://icd.who.int/browse11/l-m/en
World Health Organization. (2024). Clinical descriptions and diagnostic requirements for ICD-11 mental, behavioural and neurodevelopmental disorders. https://www.who.int/publications/i/item/9789240077263
YouGov. (2026). German National Omnibus. https://yougov.com/business/products/serviced-surveys/germany-national-omnibus
Zhang, G., Fairchild, A. J., Zhang, B., Shi, D., & Shi, D. (2026). Comparing Likert and slider response formats in clinical assessment: Evidence from measuring depression symptoms using CES-D 8. Assessment, 33(3), 388–407. https://doi.org/10.1177/10731911251329977
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