From tweets to traits: Profiling hate speech propagators on social media

The proliferation of hate speech on social media poses significant challenges for individuals, communities, and platforms. While previous approaches focused predominantly on content analysis, this study shifts the lens to user-level profiling to distinguish hate speech propagators from normal users. A dataset was curated from 2020 to 2024 from Twitter, analyzing various user attributes, including account age, follower-to-following ratio, media sharing behavior, sentiment in posts, and interaction patterns. These findings reveal that hate speech propagators have a distinct profile that separates them from normal users. These distinctions underscore the importance of incorporating behavioral metrics alongside content analysis for improved hate speech detection. By integrating the insights from this study, social media platforms can enhance their moderation strategies, enable early identification of hate speech propagators and foster a more constructive online environment. This study provides a foundation for developing advanced algorithms and policy reforms aimed at effectively combating hate speech on social media.