Comparative Analysis of Machine Learning Algorithms for Predicting Difficulty Disengaging from Social Media Among Students
Year: 2026
Type: article
Study design: Observational Study
Abstract: Social media use can adversely affect students’ academic functioning and psychological well-being when disengagement becomes difficult. This study compared Random Forest (RF), XGBoost, and Support Vector Machine (SVM) for predicting self-reported difficulty disengaging from social media among university students. A cross-sectional survey dataset comprising 118 responses was analyzed, including 75 positive (63.6%) and 43 negative (36.4%) cases. The binary target represented a behavioral proxy rather than a clinically validated diagnosis of social media addiction. Using balanced class weighting and 10-fold stratified cross-validation, SVM achieved the highest mean accuracy (77.2%) and ROC-AUC (0.804), followed by RF (73.0%) and XGBoost (66.8%). However, the performance difference between SVM and RF was not statistically significant (Nadeau–Bengio corrected *p* = 0.102), while RF demonstrated greater resampling stability. The findings provide preliminary evidence for algorithm selection in behavioral proxy classification and support future digital well-being research. Interpretation is limited by the small, single-institution sample and single-item outcome measure; therefore, the results should not be used for clinical screening or institutional deployment without external validation using established psychometric instruments.
Institutions: Institut Sains dan Bisnis Atma Luhur, Binus University
Language: English
Subfield: Sociology and Political Science
Field: Social Sciences
Domain: Social Sciences
Open Access status: gold
Estimated APC paid: $32
OpenAlex ID: W7223476784