Ibitoye, Ayodeji Olusegun ORCID: https://orcid.org/0000-0002-5631-8507, Ravindran, Ravindran ORCID: https://orcid.org/0009-0009-8832-6358, Afe, Oluwaseyi Funmi and Abiodun, Adeyinka O. (2025) Predicting and mitigating digital addiction with Machine Learning Models for improved mental health outcomes. Journal of Social Computing, 6 (4). pp. 359-377. ISSN 2688-5255 (Online) (doi:10.23919/JSC.2025.0020)

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Abstract

The exponential growth of social media has heightened concerns about digital addiction and its mental health consequences, particularly among younger populations. Existing digital health tools, including conversational agents and large language models, offer real-time support but often neglect the predictive value of structured behavioural data. This study introduces a machine learning framework to assess digital addiction risk using 3,200 anonymised self-reports comprising screen time, social media engagement, sleep duration, and mental health indicators. Across multiple models, CatBoost achieved the highest performance (precision = 85.4%, ROC-AUC = 0.93), outperforming XGBoost and Graph Neural Networks. A linear regression model provided interpretable correlations between behavioural variables and addiction risk. Structural Equation Modelling (SEM) revealed that anxiety (GAD-7) and depression (PHQ-9) mediate the relationship between digital behaviours and addiction risk, offering causal insights into these pathways. Feature importance analysis identified excessive screen time, frequent social media checking, and reduced sleep as the most influential predictors. To translate findings into practice, K-means clustering generated behavioural risk profiles, enabling personalised, data-driven recommendations. While clinical validation remains a next step, this framework demonstrates how predictive modelling and clustering can inform scalable, non-invasive digital health interventions. By integrating machine learning with causal modelling and personalised intervention design, this study advances computational approaches to digital addiction and contributes to the broader discourse on artificial intelligence applications in mental health and social computing.

Item Type:	Article
Uncontrolled Keywords:	digital addiction; machine learning (ML); mental health; personalised recommendations; social media behaviour; predictive modelling; digital wellness
Subjects:	B Philosophy. Psychology. Religion > BF Psychology Q Science > Q Science (General) Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Faculty / School / Research Centre / Research Group:	Faculty of Education, Health & Human Sciences Faculty of Education, Health & Human Sciences > School of Human Sciences (HUM) Faculty of Engineering & Science Faculty of Engineering & Science > School of Computing & Mathematical Sciences (CMS)
Last Modified:	14 Jan 2026 10:33
URI:	https://gala.gre.ac.uk/id/eprint/52281

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