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.

Predicting users’ interests on social networks is gaining attention due to its potential to cater customized information and services to the end users. Although previous works have extensively explored how users’ interests can be modeled on social networks, there has been limited investigation into the prediction of users’ future interests. The objective of our work in this paper is to empirically study the effectiveness of different sets of features based on users’ past social interactions, historical interests and their temporal dynamics to predict their interests over a collection of future-yet-unobserved topics. More specifically, we introduce and formalize the features for interest prediction in four categories: user-based, topical, explicit user-topic engagement, and friends’ influence. We further explore the influence of temporality by augmenting features with information pertaining to users’ historical interests and social connections. We model the task of future interest prediction as a learning-to-rank problem where different features and their related categories are ranked based on their relevance and performance in interest prediction, and investigate the efficiency of different features individually and comparatively for predicting the future interest of users with different activity levels in social networks over on unobserved topics. After conducting experiments on a real-world dataset sourced from Twitter, we have identified several noteworthy findings: (1) relevance feature in the category of past explicit user-topic engagement is the strongest indicator for predicting user’s future interest across all user groups, with an observed 8.57% decrease in NDCG and an 8.95% decrease in MAP when it is removed in the ablation study. (2) the observation of an 8.06% decrease in NDCG and a 7.3% decrease in MAP, when topical features such as popularity, freshness, and coherence are removed in the ablation study, highlights their significance as among the strongest indicators for users’ future interest, particularly for low-active users. (3) although temporal features show a clear positive impact across user groups with varying levels of activity (resulting in a 4.5% decrease in NDCG and a 7.3% decrease in MAP when removed in the ablation study), the temporal topical features do not demonstrate a significant positive effect, and 4) The removal of user-specific characteristics such as influence and personality traits in the ablation study reveals their significant impact in predicting future interest over cold topics, reflected by a 5.49% decrease in NDCG and a 5.72% decrease in MAP. Our findings make significant contributions to the field of future interest prediction, offering valuable insights and practical implications for various applications in social network analysis.

Abstract Social interactions through online social media have become a daily routine of many, and the number of those whose real world (offline) and online lives have become intertwined is continuously growing. As such, the interplay of individuals' online and offline activities has been the subject of numerous research studies, the majority of which explored the impact of people's online actions on their offline activities. The opposite direction of impact—the effect of real‐world activities on online actions—has also received attention but to a lesser degree. To contribute to the latter form of impact, this paper reports on a quasi‐experimental design study that examined the presence of causal relations between real‐world activities of online social media users and their online emotional expressions. To this end, we have collected a large dataset (over 17K users) from Twitter and Foursquare, and systematically aligned user content on the two social media platforms. Users' Foursquare check‐ins provided information about their offline activities, whereas the users' expressions of emotions and moods were derived from their Twitter posts. Since our study was based on a quasi‐experimental design, to minimize the impact of covariates, we applied an innovative model of computing propensity scores. Our main findings can be summarized as follows: (a) users' offline activities do impact their affective expressions, both of emotions and moods, as evidenced in their online shared textual content; (b) the impact depends on the type of offline activity and if the user embarks on or abandons the activity. Our findings can be used to devise a personalized recommendation mechanism to help people better manage their online emotional expressions.

The first international workshop on Mining Actionable Insights from Social Networks (MAISoN'17) is to be held on February 10, 2017; co-located with the Tenth ACM International Web Search and Data Mining (WSDM) Conference in Cambridge, UK. MAISoN'17 aims at bringing together researchers and participants from different disciplines such as computer science, big data mining, machine learning, social network analysis and other related areas in order to identify challenging problems and share ideas, algorithms, and technologies for mining actionable insight from social network data. We organized a workshop program that includes the presentation of eight peer-reviewed papers and keynote talks, which foster discussions around state-of-the-art in social network mining and will hopefully lead to future collaborations and exchanges.