susbed Selçuk Üniversitesi Sosyal Bilimler Enstitüsü Dergisi 2667-4750 Selcuk University 10.52642/susbed.1854677 Communication Technology and Digital Media Studies İletişim Teknolojisi ve Dijital Medya Çalışmaları Kişilik Özelliklerinin Sosyal Medya Bağımlılığı Üzerindeki Etkisi The Effect of Personality Traits on Social Media Addiction https://orcid.org/0000-0001-5871-4928 Çöllü Ersen Fazıl SELCUK UNIVERSITY, SOCIAL SCIENCES VOCATIONAL SCHOOL https://orcid.org/0000-0001-6802-9502 Yörük Eyüp Erdal SELCUK UNIVERSITY, SOCIAL SCIENCES VOCATIONAL SCHOOL https://orcid.org/0000-0002-1678-0609 Summak Mehmet Erhan SELCUK UNIVERSITY, SOCIAL SCIENCES VOCATIONAL SCHOOL 04 30 2026 59 264 283 01 02 2026 03 31 2026 Copyright © 1992, The Journal of Selcuk University Social Sciences Institute 1992 The Journal of Selcuk University Social Sciences Institute

Sosyal medyanın günlük hayata entegrasyonu, sosyal medya bağımlılığı ve dijital tükenmişlik gibi önemli psikolojik zorlukları beraberinde getirmiştir. Bu çalışma, Konya'da 798 katılımcıdan oluşan bir örneklemde Beş Faktör Kişilik Özelliklerinin sosyal medya bağımlılığı üzerindeki yordayıcı gücünü incelemiştir. Veriler, Sosyal Medya Bağımlılığı Ölçeği (SMBÖ) ve Beş Faktör Kişilik Envanteri aracılığıyla toplanmış; yapısal geçerlilik Doğrulayıcı Faktör Analizi (DFA) ile teyit edilmiştir. Çoklu doğrusal regresyon analizi, kişilik çerçevesinin bağımlılığı anlamlı düzeyde yordadığını ve toplam varyansın %19,4'ünü (R² = 0,194) açıkladığını ortaya koymuştur. Metodolojik güvenilirlik, VIF değerleri (1,15–1,32) ve gözlemlerin bağımsızlığına yönelik prosedürel önlemlerle sağlanmış; kesitsel (cross-sectional) çerçevede Durbin-Watson istatistiği (0,834) gerekçelendirilmiştir. Sorumluluk (Beta = -0,301, p < 0,001) ve Uyumluluk (Beta = -0,166, p = 0,001) temel koruyucu faktörler olarak belirlenirken; Deneyime Açıklık (Beta = 0,119, p = 0,023) ve Nevrotiklik (Beta = 0,106, p = 0,038) anlamlı risk faktörleri olarak ortaya çıkmıştır. Bu bulgular, dijital refahta bireysel kişilik farklılıklarının kritik rolünü vurgulamakta ve müdahale stratejilerinin algoritmik çağda psikolojik dayanıklılığı ve öz düzenleme becerilerini artırmaya odaklanması gerektiğini göstermektedir.

This study investigated the predictive power of the Big Five personality traits on social media addiction among 798 participants in Konya, Türkiye. Structural validity of the Social Media Addiction Scale (SMAS) and the Five-Factor Personality Inventory was confirmed through Confirmatory Factor Analysis (CFA). Multiple linear regression analysis indicates that personality traits significantly predict addiction and account for 19.4% (R² = .194) of the total variance. Methodological robustness was confirmed through VIF values (1.15–1.32) and procedural safeguards for the independence of observations to justify the Durbin-Watson statistic (0.834) within the cross-sectional framework. Conscientiousness (Beta = -0.301, p < 0.001) and Agreeableness (Beta = -0.166, p = 0.001) appeared to be protective factors while Openness to Experience (Beta = 0.119, p = 0.023) and Neuroticism (Beta = 0.106, p = 0.038) emerged as significant risk factors. These findings suggest that individual personality differences are notable determinants of digital well-being and imply that interventions should focus on enhancing self-regulation and psychological resilience in algorithmic environments.

 Social Media Addiction Big Five Personality Traits Algorithmic Burnout Digital Resilience Technostress Sosyal Medya Bağımlılığı Kişilik Özellikleri Beş Faktör Kişilik Modeli Algoritmik Tükenmişlik Dijital Stres Akbari, H. H., Gholizadeh, M. H., & Azbari, M. E. (2024). The role of utility in investment patterns: Identifying dimensions and its impact on financial decisions. Business, Marketing, and Finance Open. https://www.semanticscholar.org/paper/28ddea5f065df28f0af0e1386c5f78a62eec0d96 Akdeniz, S. (2022). Personality traits and narcissism in social media predict social media addiction. Ahmet Keleşoğlu Eğitim Fakültesi Dergisi, 28(1), 1-20. https://doi.org/10.38151/akef.2022.14 Althuwayb, S. M., & Badawi, N. (2023). Entrepreneurial intention of Saudi women in the Covid-19 pandemic era: The role of personality traits. International Journal of Professional Business Review, 8(6), e02211. https://doi.org/10.26668/businessreview/2023.v8i6.2211 Asad, K., Ali, F., & Awais, M. (2022). Personality traits, narcissism and TikTok addiction: A parallel mediation approach. International Journal of Media and Information Literacy, 7(2), 293-304. Balaskas, S., Konstantakopoulou, M., Yfantidou, I., & Komis, K. (2025). Algorithmic burnout and digital well-being: Modelling young adults’ resistance to personalized digital persuasion. Societies, 15(8), 232. https://doi.org/10.3390/soc15080232 Cannito, L., Ceccato, I., Annunzi, E., Aless, Bortolotti, R., D’Intino, E., Palumbo, R., D’Addario, C., et al. (2023). Bored with boredom? Trait boredom predicts internet addiction through the mediating role of attentional bias toward social networks. Frontiers in Human Neuroscience, 17, 1179142. https://doi.org/10.3389/fnhum.2023.1179142 Chi, L., Tang, T., & Tang, E. (2022). Psychometric properties of the Utrecht Work Engagement Scale for Students (UWES-S) in the Taiwanese context. Current Psychology, 42, 27428-27441. https://doi.org/10.1007/s12144-022-03737-0 Costa, P. T., Jr., & McCrae, R. R. (1992). Revised NEO Personality Inventory (NEO-PI-R) and NEO Five-Factor Inventory (NEO-FFI) professional manual. Psychological Assessment Resources. Cronbach, L. J. (2004). My current thoughts on coefficient alpha and successor procedures. Educational and Psychological Measurement, 64(3), 391-418. https://doi.org/10.1177/0013164404266386 Döring, N. (2022). Visual gender stereotypes (Advertisement, Social Media). DOCA - Database of Variables for Content Analysis. https://doi.org/10.34778/5i Erdem, A., Şahin, R., & Alkan, M. F. (2024). Personality traits as the predictors of eudaimonic well-being in undergraduates. Educational Academic Research, 53, 137-151. https://doi.org/10.33418/education.1421847 Esposito, G., Perla, V., Passeggia, R., Fertuck, E., & Mergenthaler, E. (2020). Reflective functioning and personal recovery process of users with borderline personality disorder on Instagram: An explorative study using computerized and thematic analysis. Research in Psychotherapy: Psychopathology, Process, and Outcome, 23(2), 463. https://doi.org/10.4081/ripppo.2020.463 Evangelou, S. M., Michanetzi, E. L., & Xenos, M. (2024). Exploring the impact of negative online feedback on well-being: A comprehensive analysis incorporating Big-Five personality traits and physiological responses. Computers in Human Behavior Reports, 15, 100457. https://doi.org/10.1016/j.chbr.2024.100457 Fineberg, N., Menchón, J. M., Hall, N., Dell’Osso, B., Br, M., Potenza, M. N., Chamberlain, S. R., et al. (2022). Advances in problematic usage of the internet research – A narrative review by experts from the European network for problematic usage of the internet. Comprehensive Psychiatry, 118, 152346. https://doi.org/10.1016/j.comppsych.2022.152346 George, D., & Mallery, P. (2019). IBM SPSS Statistics 26 step by step: A simple guide and reference. Routledge. https://doi.org/10.4324/9780429056765 Gosling, S. D., Rentfrow, P. J., & Swann, W. B., Jr. (2003). A very brief measure of the Big-Five personality domains. Journal of Research in Personality, 37(6), 504-528. https://doi.org/10.1016/S0092-6566(03)00046-1 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 Hooper, D., Coughlan, J., & Mullen, M. (2008). Evaluating model fit: A synthesis of the structural equation modelling literature. Proceedings of the 7th European Conference on Research Methodology for Business and Management Studies, 2, 195-200. Hu, L. T., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6(1), 1-55. https://doi.org/10.1080/10705519909540118 Hussain, A. M., Khan, H., Ajaml, I., & Akhtar, Y. (2023). Relationship of narcissism, machiavellianism, and psychopathy personality traits with social media addiction among adults: Gender and marital status are in focus. Journal of Social Sciences Review, 3(2), 1012-1021. https://doi.org/10.54183/jssr.v3i2.334 Ibrahim, R. K., Khaled, M., Almansoori, M., Almazrouei, M., Ashraf, A., Alahmedi, S. H., & Hendy, A. (2025). Screen time and stress: Understanding how digital burnout influences health among nursing students. BMC Nursing, 24(1), 990. https://doi.org/10.1186/s12912-025-03621-9 Jamshidi, S., Noferesti, A., & Farahani, H. (2024). Examining the relationship between problematic social media use and dark personality traits with the mediating role of emotion regulation and self-compassion. Journal of Mental Health, 2, 49-59. https://doi.org/10.61838/kman.hn.2.3.7 Jitoku, D., Kobayashi, N., Fujimoto, Y., Qian, C., Okuzumi, S., Tei, S., Matsuyoshi, D., et al. (2024). Explicit and implicit effects of gaming content on social media on the behavior of young adults. Frontiers in Psychology, 15, 1332462. https://doi.org/10.3389/fpsyg.2024.1332462 John, O. P., Donahue, E. M., & Kentle, R. L. (1991). The Big Five Inventory—Versions 4a and 54. Institute of Personality and Social Research. Karadeniz, S., Büyüköztürk, Ş., Akgün, Ö. E., Çakmak, E. K., & Demirel, F. (2008). The Turkish adaptation study of Motivated Strategies for Learning Questionnaire (MSLQ). Online Submission, 7(4), 1-15. Karasar, N. (2012). Bilimsel araştırma yöntemi (24. bs.). Ankara: Nobel Yayıncılık. Kardefelt-Winther, D. (2014). A conceptual and methodological critique of internet addiction research: Towards a model of compensatory internet use. Computers in Human Behavior, 31, 351-354. https://doi.org/10.1016/j.chb.2013.10.059 Kashefian-Naeeini, S., Zarifsanaiey, N., & Mehrabi, M. (2025). The impact of learner background variables on academic burnout in online vs. face-to-face classes. Frontiers in Psychology, 16, 1484760. https://doi.org/10.3389/fpsyg.2025.1484760 Katz, E., Blumler, J. G., & Gurevitch, M. (1973). Uses and gratifications research. The Public Opinion Quarterly, 37(4), 509-523. https://doi.org/10.1086/268109 Kelloway, E. K. (1998). Using LISREL for structural equation modeling: A researcher's guide. Sage. LaRose, R., Lin, C. A., & Eastin, M. S. (2003). Unregulated Internet usage: Addiction, habit, or deficient self-regulation?. Media Psychology, 5(3), 225-253. https://doi.org/10.1207/S1532785XMEP0503_01 Leech, N. L., Barrett, K. C., & Morgan, G. A. (2014). SPSS for intermediate statistics: Use and interpretation. Routledge. Litan, D. E. (2025). Mental health in the “era” of artificial intelligence: technostress and the perceived impact on anxiety and depressive disorders—an SEM analysis. Frontiers in Psychology, 16, 1600013. https://doi.org/10.3389/fpsyg.2025.1600013 Lou, Q., & Xu, W. (2025). Personality modeling for persuasion of misinformation using AI agent. ArXiv. https://doi.org/10.48550/arXiv.2501.08985 Minutillo, A., Trana, A. D., Aquilina, V., Ciancio, G. M., Berretta, P., & Maida, N. L. (2024). Recent insights in the correlation between social media use, personality traits and exercise addiction: A literature review. Frontiers in Psychiatry, 15, 1392317. https://doi.org/10.3389/fpsyt.2024.1392317 Nakshine, V. S., Thute, P. P., Khatib, M. N., & Sarkar, B. (2022). Increased screen time as a cause of declining physical, psychological health, and sleep patterns: A literary review. Cureus, 14(10), e30051. https://doi.org/10.7759/cureus.30051 Pautrat, M., Guen, A. L., Barrault, S., Ribadier, A., Ballon, N., Lebeau, J., & Brunault, P. (2022). Impulsivity as a risk factor for addictive disorder severity during the COVID-19 lockdown. International Journal of Environmental Research and Public Health, 20(1), 705. https://doi.org/10.3390/ijerph20010705 Qiu, S., Qiu, J., Xu, J., & Wang, L. (2025). Effective emotion regulation and positive psychological capital as coping strategies to alleviate teacher burnout. Frontiers in Psychology, 16, 1639037. https://doi.org/10.3389/fpsyg.2025.1639037 Rengifo, E. F. Q., Rojas, L. B. R., & Rueda, M. D. (2021). Relationship between planned behavior and social media impact. Semantics Scholar. https://www.semanticscholar.org/paper/250b3d47aa094ee8a6081d80d5af42930396e983 Schermelleh-Engel, K., Moosbrugger, H., & Müller, H. (2003). Evaluating the fit of structural equation models: Tests of significance and descriptive goodness-of-fit measures. Methods of Psychological Research Online, 8(2), 23-74. Şahin, C., & Yağcı, M. (2017). Sosyal medya bağımlılığı ölçeği-yetişkin formu: geçerlilik ve güvenirlik çalışması [Social media addiction scale-adult form: A validity and reliability study]. Ahi Evran Üniversitesi Kırşehir Eğitim Fakültesi Dergisi, 18(1), 523-538. https://izlik.org/JA87TW45UC Şimşek, Ö. F. (2020). Yapısal eşitlik modellemesine giriş: Temel ilkeler ve LISREL uygulamaları. Nobel Akademik Yayıncılık. Ünlü, S., Uzun, K., & Arslan, G. (2025). Mindfulness-based intervention in schools: Addressing social media burnout and enhancing well-being in adolescents. Children, 12(7), 826. https://doi.org/10.3390/children12070826 Valkenburg, P. M., Schouten, A. P., & Peter, J. (2005). Adolescents’ identity experiments on the internet. New Media & Society, 7(3), 383-402. https://doi.org/10.1177/1461444805052282