Title: A Web of Hate: Tackling Hateful Speech in Online Social Spaces,
Abstract: 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.