Comparative Analysis of Machine Learning Regression Algorithms for Predicting Social Media Addiction Levels Among University Students
Keywords:
Machine Learning, Linear Regression, Social Media Addiction, Prediction, University StudentsAbstract
An increased use of social media platforms by university students has raised some concerns about their possible addiction, which might result in negative effects on their mental health, sleep and academic achievement. Addiction assessment to social media can be done through questionnaire-based tools that cannot effectively capture the interrelations between variables. In this study, the development and evaluation of machine learning regression models for predicting social media addiction of university students will be discussed. The dataset comprises the survey data having the dependent variable, Addicted Score, and three independent variables: Average Daily Usage Hours, Mental Health Score, and Sleep Hours per Night. Three regression algorithms, including Linear Regression, Decision Tree Regression and Support Vector Machine (SVM) have been compared using Orange Data Mining software. Models have been evaluated based on the fivefold cross-validation with five performance metrics: MSE, RMSE, MAE, MAPE and R². Based on the experiment, it was found that Linear Regression had the highest accuracy with R² of 0.915, MSE of 0.215, and RMSE of 0.464. The R² score of Decision Tree Regression was found to be 0.907, while for SVM it was 0.879. It can be seen from these results that there is linearity amongst the relationships between the different variables in the dataset which made Linear Regression give superior generalization performance over other regressors. This paper has shown that the use of regression using machine learning gives an efficient way of predicting the level of social media addiction amongst university students.
References
[1] Pellegrino, A., Stasi, A., & Bhatiasevi, V. (2022). Research trends in social media addiction and problematic social media use: A bibliometric analysis. Frontiers in psychiatry, 13, 1017506.
[2] Thity, N. T., Rahman, A., Dulmini, A., Yasmin, M. N., & Rois, R. (2025). An illustration of multi-class roc analysis for predicting internet addiction among university students. Plos one, 20(7), e0325855.
[3] Azzolina, D., Secchettin, E., Berchialla, P., Zhu, T., Şentürk Acar, A., & Gregori, D. (2025). Machine learning approaches for monitoring mental health and substance abuse using social media data. Frontiers in Public Health, 13, 1590564.
[4] Xiao, Y., Meng, Y., Brown, T. T., Keyes, K. M., & Mann, J. J. (2025). Addictive screen use trajectories and suicidal behaviors, suicidal ideation, and mental health in US youths. Jama, 334(3), 219-228.
[5] Nurmala, I., Nadhiroh, S. R., Pramukti, I., Tyas, L. W., Zari, A. P., Griffiths, M. D., & Lin, C. Y. (2022). Reliability and validity study of the Indonesian Smartphone Application-Based Addiction Scale (SABAS) among college students. Heliyon, 8(8).
[6] Roque Herrera, Y., Alonso-García, S., Tenelanda López, D. V., & López Núñez, J. A. (2025). Predictive Capacity of Social Media Addiction on Academic Engagement in University Students. Education Sciences, 15(12), 1677.
[7] Sutrisni, D. S., Alisyahbana, M. I., Al-hakim, M. L., & Prasetyo, D. (2025). An Analysis of the Impact of Social Media Addiction on Students’ Academic Performance Using K-Means and Decision Tree. Journal of Artificial Intelligence and Legal Technology, 1(1), 15-25.
[8] Zhang, Y., Wang, Z., Ding, Z., Tian, Y., Dai, J., Shen, X., ... & Cao, Y. (2025). Employing machine learning and deep learning models for mental illness detection. Computation, 13(8), 186.
[9] Hou, Y., Xiong, D., Jiang, T., Song, L., & Wang, Q. (2019). Social media addiction: Its impact, mediation, and intervention. Cyberpsychology: Journal of psychosocial research on cyberspace, 13(1).
[10] Ehsan, T., & Basit, J. (2024). Machine learning for detecting social media addiction patterns: Analyzing user behavior and mental health data. Int. J. Inf. Sci. Technol.(IJIST), 6(4), 1789-1807.
[11] Johar, G., & Patel, R. (2024, July). A Machine Learning Model for Predicting Internet Addiction in Teenage and Young Students. In 2024 IEEE 3rd World Conference on Applied Intelligence and Computing (AIC) (pp. 285-291). IEEE.
[12] Phipps, D. J., Brown, D. J., Hagger, M. S., & Hamilton, K. (2024). Can the cans: determinants of container deposit behavior before and after introduction of a container refund scheme. Behavioral Sciences, 14(2), 112.
[13] Kim, K., Yoon, Y., & Shin, S. (2024). Explainable prediction of problematic smartphone use among South Korea's children and adolescents using a Machine learning approach. International journal of medical informatics, 186, 105441..
[14] Demšar, J., Curk, T., Erjavec, A., Gorup, Č., Hočevar, T., Milutinovič, M., & Zupan, B. (2013). Orange: data mining toolbox in Python. the Journal of machine Learning research, 14(1), 2349-2353.
[15] Dobesova, Z. (2024). Evaluation of Orange data mining software and examples for lecturing machine learning tasks in geoinformatics. Computer Applications in Engineering Education, 32(4), e22735.
[16] Wang, X., Zhang, E., Cui, Y., Huang, J., & Cheng, M. (2024). Predicting internet addiction in college students using a 1D-CNN model: analysis of influencing factors. Dyna, 91(233), 66-74.
[17] Guo, C., Chen, M., Ji, X., Li, J., Ma, Y., & Zang, S. (2025). Association between internet addiction and sleep quality in medical students: a longitudinal study. Frontiers in Psychology, 16, 1517590.
[18] Tetik, S., Popescu, C., & Akkaya, B. (2025). Social media addiction and social skills: Implications for societal learning systems, technology, social economy, and societal challenges. Systems, 13(7), 501.
[19] Chandra, R., Asril, B. W., & Aritonang, M. A. S. (2025). Decision Tree-Based Approach for Predicting Mental Health Symptoms in University Students: A Case Study at IT Del. Journal of Intelligent Computing, Systems, and Applications (JICSA), 1(01), 20-29.
[20] Ali, A., Hosain, M. S., Siddik, M. A. B., Hasan, M., Habib, M. A., Kabir, M. A., & Mahmud, A. (2025). Classifying Internet Addiction Using Machine Learning Approach: A Study Among Adolescents in Bangladesh. Public Health Challenges, 4(4), e70165.
[21] Shaikh, I. A. K., Jayachandran, N., Chouhan, D. S., Priya, S., Perada, A., & Maharishi, M. (2025, July). Behavioral and Mental Health Analysis for Social Media Addiction Detection Using Hybrid RBF-SVM Models. In 2025 3rd World Conference on Communication & Computing (WCONF) (pp. 1-6). IEEE.
[22] Bin Rofi, I., Eshita, M. M., Ahmed, M. S., & Noor, J. (2024, December). Identifying Influences: A Machine Learning and Explainable AI Approach to Analyzing Social Media Addiction Resulting from Academic Frustration. In Proceedings of the 11th International Conference on Networking, Systems, and Security (pp. 128-136).
[23] Diamantopoulou, M. J., & Papamichail, D. M. (2024). Performance evaluation of regression-based machine learning models for modeling reference evapotranspiration with temperature data. Hydrology, 11(7), 89.
[24] Chicco, D., Warrens, M. J., & Jurman, G. (2021). The coefficient of determination R-squared is more informative than SMAPE, MAE, MAPE, MSE and RMSE in regression analysis evaluation. Peerj computer science, 7, e623.
Downloads
Published
Issue
Section
License
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
