User-Centric Modeling of Online Hate Through the Lens of Psycholinguistic Patterns and Behaviors in Social Media

Hate speech in social media is a growing problem that reinforces racial discrimination and mistrust between people, leading to physical crimes, violence, and fragmentation in world communities. Although previous studies showed the potential of user profiling in hate speech detection in social media, there has not been a thorough analysis of users' characteristics and dispositions to understand the development of hate attitudes among users. To bridge this gap, we investigate the role of a wide range of psycholinguistic and behavioral traits in characterizing and distinguishing users prone to post hate speech on social media. Considering anti-Asian hate during the COVID-19 pandemic as a case study, we curate a dataset of 5 417 041 tweets from 3001 Twitter users prone to publish hate content (aka hateful-to-be users) and a corresponding matched set of 3001 control users. Our findings reveal significant statistical differences in most dimensions of psycholinguistic attributes and online activities of hateful-to-be users compared to control users. We further develop a classifier and demonstrate that features derived from user timelines are strong indicators for automatically predicting the onset of hateful behavior.