paper

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

arXiv:1709.10159

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.

arXiv comment9 pages, presented at the first workshop on Text Analytics for Cybersecurity and Online Safety (TA-COS), collocated with LREC 2016

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