Offline events and online hate

Online hate speech is a critical and worsening problem, with extremists using social media platforms to radicalize recruits and coordinate offline violent events. While much progress has been made in analyzing online hate speech, no study to date has classified multiple types of hate speech across both mainstream and fringe platforms. We conduct a supervised machine learning analysis of 7 types of online hate speech on 6 interconnected online platforms. We find that offline trigger events, such as protests and elections, are often followed by increases in types of online hate speech that bear seemingly little connection to the underlying event. This occurs on both mainstream and fringe platforms, despite moderation efforts, raising new research questions about the relationship between offline events and online speech, as well as implications for online content moderation.

Automated summary

Online hate speech is a growing problem, as extremists exploit social media platforms for recruitment and coordination of offline violence. Existing studies have focused on analyzing online hate speech, but have not classified different types across mainstream and fringe platforms. By using machine learning, we analyze 7 types of hate speech on 6 interconnected online platforms. Our findings reveal that offline trigger events lead to increases in various types of hate speech, which may not have a direct connection to the underlying events. This raises new research questions about the relationship between offline events and online speech, as well as the implications for content moderation.