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.

Online hate speech has become a critical issue, particularly during the COVID-19 pandemic, when anti-Asian sentiment surged across social media platforms. However, the causal mechanisms driving emotional and behavioral shifts in users posting hateful content remain understudied. This study investigates the causal relationship between engaging in hateful content and changes in linguistic and emotional expression on social media. Using a dataset of 6,002 Twitter/X users, we employ causal inference techniques, including propensity score matching, and advanced topic modeling to compare users posting hateful content with a matched group of non-hateful users. Our main findings can be summarized as follows: (a) Users who post hateful content show significantly higher levels of anger, anxiety, and negative emotions, along with increased third-person pronoun usage. (b) Moral outrage and profanity levels peak during hateful posts but decline over time, while remaining elevated compared to non-hateful posts. (c) Hateful posts are more interconnected, cover more diverse topics, and are more similar to one another, revealing lower cohesion within individual posts but higher cohesion across posts. These findings contribute to understanding the causal effects of online hate speech on user behavior, offering actionable insights for social media platforms to mitigate the spread of hateful content and its broader societal impact. • Causal inference reveals emotional and linguistic shifts in 6,002 hate speech users. • Hate speech users show heightened anger, anxiety, and fewer positive expressions. • Increased third-person pronouns indicate greater social detachment in hate speech. • Moral outrage and profanity decline over time but stay above control group levels. • Hate speech narratives form cohesive networks with high global cohesion, low specificity.

The intersection of music and mental health has gained increasing attention, with previous studies highlighting music’s potential to reduce stress and anxiety. Despite these promising findings, many of these studies are limited by small sample sizes and traditional observational methods, leaving a gap in our understanding of music’s broader impact on mental health. In response to these limitations, this study introduces a novel approach that combines generalized linear mixed models (GLMM) with propensity score matching (PSM) to explore the relationship between music listening and stress levels among social media users diagnosed with anxiety, depression, and posttraumatic stress disorder (PTSD). Our research not only identifies associative patterns between music listening and stress but also provides a more rigorous examination of potential causal effects, taking into account demographic factors such as education level, gender, and age. Our findings reveal that across all mental health conditions, music listening is significantly associated with reduced stress levels, with an observed 21.3% reduction for anxiety, 15.4% for depression, and 19.3% for PTSD. Additionally, users who listened to music were more likely to report a zero stress score, indicating a stronger relaxation effect. Further, our analysis of demographic variations shows that age and education level influence the impact of music on stress reduction, highlighting the potential for personalized interventions. These findings contribute to a deeper understanding of music’s therapeutic potential, particularly in crafting interventions tailored to the diverse needs of different populations.