The Dynamic Association between Sleep Quality, Suicide Risk, and Perceived Social Support: Based on Social Media Data

Purpose: Suicide risk and sleep problems are significant public health concerns, with perceived social support acting as a key social determinant of mental health. This study aimed to investigate the dynamic, reciprocal relationships among public sleep quality (SQ), suicide risk (SR), and perceived social support (SS) from a macro-level, epidemiological perspective to understand their systemic interplay over time. Methods: We constructed a provincial-level annual panel dataset using public data from Weibo, a major Chinese social media platform, spanning 14 years (2010–2023) across 31 provinces. The annual frequencies of keywords related to SQ, SR, and SS were calculated to serve as population-level indicators. Dynamic Structural Equation Modeling (DSEM) was employed to analyze the autoregressive, cross-lagged, and contemporaneous effects among these variables. Results: All three variables demonstrated significant temporal continuity. Longitudinally, higher prior SR predicted subsequent declines in both SQ and SS. Paradoxically, however, better prior SQ (i.e., lower scores on the measure) predicted higher subsequent SR, while higher prior SS predicted poorer subsequent SQ. Concurrently, poorer SQ and lower SS were associated with higher SR. Conclusions: Utilizing large-scale social media data, this study uncovers the complex feedback loops governing the population-level dynamics of sleep quality, perceived social support, and suicide risk. The findings highlight the necessity of a systemic public health approach for suicide prevention that considers these complex, time-dependent, and sometimes paradoxical interactions, moving beyond simplistic linear risk models.