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

TL;DR

This paper introduces a novel method for detecting hateful speech in online social platforms by leveraging content from self-identifying hateful communities, overcoming limitations of keyword-based detection and improving accuracy.

Contribution

The paper proposes a community-based training approach for hateful speech detection that outperforms existing keyword-based methods across multiple platforms.

Findings

Significantly better detection accuracy than keyword-based methods

Effective across various social media platforms

Reduces reliance on expensive manual annotation

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.