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Online social platforms are beset with hateful speech - content thatexpresses hatred for a person or group of people. Such content can frighten,intimidate, or silence platform users, and some of it can inspire other usersto commit violence. Despite widespread recognition of the problems posed bysuch content, reliable solutions even for detecting hateful speech are lacking.In the present work, we establish why keyword-based methods are insufficientfor detection. We then propose an approach to detecting hateful speech thatuses content produced by self-identifying hateful communities as training data.Our approach bypasses the expensive annotation process often required to trainkeyword systems and performs well across several established platforms, makingsubstantial improvements over current state-of-the-art approaches.

A Web of Hate: Tackling Hateful Speech in Online Social Spaces