Social Media Fatigue and Depressive Symptoms Among Chinese University Students: Artificial Intelligence Anxiety as a Statistical Mediator and Subjective Socioeconomic Status as a Moderator

As social media and artificial intelligence (AI) become increasingly embedded in university students’ learning and daily lives, fatigue arising from established digital platforms and anxiety surrounding emerging AI may constitute interrelated forms of technology-related distress. However, how these experiences are jointly associated with depressive symptoms, and whether these associations vary by subjective socioeconomic status (SSES), remains insufficiently understood. Drawing on conservation of resources theory and research on status maintenance, this cross-sectional study examined AI anxiety as a statistical mediator of the association between social media fatigue and depressive symptoms and further tested whether SSES moderated the association between AI anxiety and depressive symptoms. Survey data from 1108 Chinese university students were analyzed using regression models with heteroscedasticity-consistent type 3 (HC3) robust standard errors and percentile-bootstrap confidence intervals. Social media fatigue was positively associated with AI anxiety, which in turn was positively associated with depressive symptoms, yielding a significant statistical indirect association. As SSES increased, the association between AI anxiety and depressive symptoms became stronger, and the corresponding conditional indirect association also increased. Overall, these findings suggest that understanding university students’ digital mental health requires attention to both distress arising across different technological environments and the subjective social positions from which these experiences are evaluated, offering a more context-sensitive perspective on how established and emerging forms of digital stress may be jointly associated with psychological well-being.