Analysis of the Impact of Violent Content on Social Media on Adolescent Cyberpsychology Using Support Vector Machine and Random Forest
DOI:
https://doi.org/10.30871/jaic.v10i1.11415Keywords:
Cyberpsychology, Violent Content, Multi-label Classification, Support Vector Machine, Random ForestAbstract
Adolescent exposure to violent content on social media has emerged as a critical issue due to its potential impact on mental health and cyberpsychological well-being. This study aims to classify multiple cyberpsychological impacts experienced by adolescents as a result of exposure to violent content on social media using a multi-label machine learning approach. A quantitative method was employed using self-reported data collected from 550 Indonesian adolescents aged 12–18 years through an online questionnaire. Psychological impacts were measured using adapted instruments from the Depression Anxiety Stress Scales (DASS-21) and cyberpsychology scales, then transformed into multi-label targets. Support Vector Machine (SVM) and Random Forest algorithms were implemented using a One-vs-Rest strategy. Model performance was evaluated using Hamming Loss, precision, recall, and Macro F1-score. The results indicate that SVM outperformed Random Forest with a Hamming Loss of 23.16% and a Macro F1-score of 0.42, particularly in predicting dominant labels such as anxiety and decreased self-confidence. However, both models showed limited performance in predicting minority labels such as depression and academic decline due to data imbalance. These findings highlight the importance of handling imbalanced data in cyberpsychology-based machine learning research and demonstrate the potential of multi-label classification in representing the complexity of psychological impacts of digital violence on adolescents.
Downloads
References
[1] S. Mader, D. Costantini, A. Fahr, and M. Delgrande, “Social Science & Medicine The effect of social media use on adolescents ’ subjective well-being : Longitudinal evidence from Switzerland,” Soc. Sci. Med., vol. 365, no. December 2024, p. 117595, 2025, doi: 10.1016/j.socscimed.2024.117595.
[2] M. Nur Cahya, W. Ningsih, and A. Lestari, “Dampak Media Sosial terhadap Kesejahteraan Psikologis Remaja: Tinjauan Pengaruh Penggunaan Media Sosial pada Kecemasan dan Depresi Remaja,” J. Sos. Teknol., vol. 3, no. 8, pp. 704–706, 2023, doi: 10.59188/jurnalsostech.v3i8.917.
[3] S. W. Fajriani et al., “Cyberspace: Dampak Penyimpangan Perilaku Komunikasi Remaja Cyberspace: The Impact of Adolescent Communication Behavior Deviation,” J. Ilmu Pengetah. dan Teknol. Komun., vol. 23, no. 1, pp. 63–78, 2021, [Online]. Available: http://dx.doi.org/10.33169/iptekkom.23.1.2021.63-78
[4] L. Winstone, S. Jamal, and B. Mars, “Cyberbullying Perpetration and Victimization as Risk Factors for Self-Harm: Results From a Longitudinal Cohort Study of 13–14-Year-Olds in England,” J. Adolesc. Heal., vol. 75, no. 2, pp. 298–304, 2024, doi: 10.1016/j.jadohealth.2024.04.004.
[5] P. Yi and A. Zubiaga, “Session-based cyberbullying detection in social media: A survey,” Online Soc. Networks Media, vol. 36, no. August 2022, p. 100250, 2023, doi: 10.1016/j.osnem.2023.100250.
[6] A. Perera and P. Fernando, “Cyberbullying Detection System on Social Media Using Supervised Machine Learning,” Procedia Comput. Sci., vol. 239, no. 2023, pp. 506–516, 2024, doi: 10.1016/j.procs.2024.06.200.
[7] I. Nissar et al., “An Intelligent Healthcare System for Automated Diabetes Diagnosis and Prediction using Machine Learning,” Procedia Comput. Sci., vol. 235, pp. 2476–2485, 2024, doi: 10.1016/j.procs.2024.04.233.
[8] K. C. Onyelowe, C. B. Mahesh, B. Srikanth, C. Nwa-david, J. Obimba-wogu, and J. Shakeri, “Support vector machine ( SVM ) prediction of coefficients of curvature and uniformity of hybrid cement modified unsaturated soil with NQF inclusion,” Clean. Eng. Technol., vol. 5, p. 100290, 2021, doi: 10.1016/j.clet.2021.100290.
[9] S. R. Khawaja et al., “Journal of Hand Surgery Global Online The Impact of Social Media for Hand Surgeons : A Prevalence and Correlation Study With Online and Academic Reputations,” J. Hand Surg. Glob. Online, pp. 1–6, 2024, doi: 10.1016/j.jhsg.2024.11.001.
[10] K. Oka, J. He, Y. Honda, and Y. Hijioka, “Random forest analysis of the relative importance of meteorological indicators for heatstroke cases in Japan based on the degree of severity and place of occurrence,” Environ. Res., vol. 263, no. P2, p. 120066, 2024, doi: 10.1016/j.envres.2024.120066.
[11] G. Obaido et al., “Machine Learning with Applications Supervised machine learning in drug discovery and development : Algorithms , applications , challenges , and prospects,” Mach. Learn. with Appl., vol. 17, no. July, p. 100576, 2024, doi: 10.1016/j.mlwa.2024.100576.
[12] Lovibond, S. H., & Lovibond, P. F. (1995). Manual for the Depression Anxiety Stress Scales. Psychology Foundation.
[13] He, H., & Garcia, E. A. (2009). Learning from imbalanced data. IEEE Transactions on Knowledge and Data Engineering.
[14] Zhang, M. L., & Zhou, Z. H. (2014). A review on multi-label learning algorithms. IEEE Transactions on Knowledge and Data Engineering.
[15] Kuss, D. J., & Griffiths, M. D. (2017). Social networking sites and addiction. International Journal of Environmental Research and Public Health.
[16] APJII. (2023). Survey Pengguna Internet di Indonesia 2022-2023.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 Wulandari Febriani, Mambang Mambang, Septyan Eka Prastya, Billy Sabella, Finki Dona Marleny
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License (Attribution-ShareAlike 4.0 International (CC BY-SA 4.0) ) that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
