Machine Learning Detection of Online Social Exclusion and Its Association with Adolescent Loneliness
Keywords:
Adolescent loneliness, online social exclusion, cyber-ostracism, machine learning, digital mental health, computational psychologyAbstract
Objective: The present study aimed to examine whether machine learning–detected online social exclusion predicts adolescent loneliness and whether computationally derived exclusion indicators provide incremental explanatory power beyond self-reported perceived exclusion.
Methods and Materials: A cross-sectional correlational design was employed with a sample of 718 adolescents aged 13–17 years recruited from secondary schools in central Mexico. Participants completed the Spanish-adapted UCLA Loneliness Scale and a validated self-report measure of online social exclusion. In addition, anonymized digital interaction data from the previous 30 days were collected and processed using natural language processing techniques. A supervised transformer-based machine learning model was trained to classify exclusionary linguistic and interactional patterns within 96,438 message entries. Individual-level online exclusion probability scores were computed based on linguistic markers, response latency asymmetry, and unanswered message ratios. Hierarchical regression analyses and structural equation modeling were conducted to test direct and indirect associations between machine learning–detected exclusion, perceived exclusion, and loneliness.
Findings: Machine learning–detected online social exclusion was positively and significantly associated with adolescent loneliness (p < 0.001). Hierarchical regression analysis demonstrated that computationally detected exclusion predicted loneliness above and beyond demographic variables and self-reported exclusion (ΔR² = 0.06, p < 0.001). Structural equation modeling indicated acceptable model fit and revealed that perceived exclusion partially mediated the relationship between machine learning–detected exclusion and loneliness (indirect effect p < 0.001), while the direct path remained significant (p < 0.001).
Conclusion: The findings indicate that algorithmically detected online exclusionary patterns constitute a significant and independent predictor of adolescent loneliness. Integrating machine learning–based behavioral analytics with psychosocial assessment enhances the precision of loneliness risk identification and offers promising avenues for early detection and prevention strategies in digitally mediated youth environments.
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