Music Listening, Mental Health, and Stress: A Computational Framework for Personalized Analysis and Recommendation

This study examines how music listening is associated with short-term stress expression among individuals with mental health conditions, including depression, anxiety, PTSD, and bipolar disorder, using large-scale social media data. We analyzed over 20 million posts from 10,264 users on Twitter (now X) and identified music-listening sessions through shared links to streaming platforms. Stress-related language was measured in tweets posted within 30–60 min after each listening event. To reduce confounding, we applied Propensity Score Matching (PSM) and modeled associations using Zero-Inflated Generalized Linear Mixed Models (ZIGLMM) across music genres and audio attributes (valence, tempo, instrumentalness). The results reveal clear mental health–specific and genre-dependent effects. For example, users with depression showed 24% higher stress 60 min after listening to pop music, while PTSD users exhibited a 33% increase after 30 min. Low-valence music was associated with delayed stress increases (e.g., 14% in depression and approximately 25% in bipolar), whereas high-valence music showed no significant stress elevation. Building on these findings, we demonstrate a proof-of-concept, stress-aware music recommendation framework that more effectively ranks stress-reducing songs (MRR = 0.35 vs. 0.18 for the base model). These findings highlight the potential of data-driven music interventions for emotional well-being.