This article examines the psychological effects of social media use and explores gender-related differences, with particular attention to issues reported by women. The analysis is informed by social comparison theory and self-determination theory to explain how digital environments influence behavior and self-perception. The study focuses on psychological outcomes such as anxiety, depressive symptoms, body image dissatisfaction, and patterns of compulsive platform use. In parallel, social media platforms generate extensive behavioral data that may support the identification of mental health risks. From a computational perspective, artificial intelligence methods – including content analysis, sentiment analysis, and machine learning classification – are examined as tools for early screening of psychological distress within digital environments. A hybrid methodological approach is applied to integrate psychological analysis with data-driven AI (artificial intelligence) techniques. The results indicate that social media use is associated with higher levels of self-reported psychological vulnerability among women, while AI-based methods demonstrate the capacity to detect mental health-related signals in digital data. From a computer science perspective, the study contributes to human-centered and responsible artificial intelligence by proposing an interdisciplinary computational framework that links multimodal digital data with psychologically grounded constructs. The article concludes by outlining possible applications of AI in digital well-being initiatives and discussing ethical considerations related to privacy, autonomy, and transparency.
Nazyrova et al. (Thu,) studied this question.