Exploring hate speech dynamics: The emotional, linguistic, and thematic impact on social media users

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.