A Web of Hate: Tackling Hateful Speech in Online Social Spaces
This addresses the challenge of hateful speech detection for online platform users and moderators, offering a more efficient solution than existing methods.
The paper tackled the problem of detecting hateful speech in online social platforms by proposing a method that uses content from self-identifying hateful communities as training data, resulting in substantial improvements over state-of-the-art approaches across several platforms.
Online social platforms are beset with hateful speech - content that expresses hatred for a person or group of people. Such content can frighten, intimidate, or silence platform users, and some of it can inspire other users to commit violence. Despite widespread recognition of the problems posed by such content, reliable solutions even for detecting hateful speech are lacking. In the present work, we establish why keyword-based methods are insufficient for detection. We then propose an approach to detecting hateful speech that uses content produced by self-identifying hateful communities as training data. Our approach bypasses the expensive annotation process often required to train keyword systems and performs well across several established platforms, making substantial improvements over current state-of-the-art approaches.