Automated Hate Speech Detection and the Problem of Offensive Language

Abstract

A key challenge for automatic hate-speech detection on social media is the separation of hate speech from other instances of offensive language. Lexical detection methods tend to have low precision because they classify all messages containing particular terms as hate speech and previous work using supervised learning has failed to distinguish between the two categories. We used a crowd-sourced hate speech lexicon to collect tweets containing hate speech keywords. We use crowd-sourcing to label a sample of these tweets into three categories: those containing hate speech, only offensive language, and those with neither. We train a multi-class classifier to distinguish between these different categories. Close analysis of the predictions and the errors shows when we can reliably separate hate speech from other offensive language and when this differentiation is more difficult. We find that racist and homophobic tweets are more likely to be classified as hate speech but that sexist tweets are generally classified as offensive. Tweets without explicit hate keywords are also more difficult to classify.

To appear in the Proceedings of ICWSM 2017. Please cite that version

Cited by in corpus (10)

• Stereotypical Bias Removal for Hate Speech Detection Task using Knowledge-based Generalizations
• Hateminers : Detecting Hate speech against Women
• "Like Sheep Among Wolves": Characterizing Hateful Users on Twitter
• Detecting Hate Speech in Social Media
• Trajectories of Blocked Community Members: Redemption, Recidivism and Departure
• A large-scale crowdsourced analysis of abuse against women journalists and politicians on Twitter
• Learning from Fact-checkers: Analysis and Generation of Fact-checking Language
• Low-Shot Classification: A Comparison of Classical and Deep Transfer Machine Learning Approaches
• Analysis of the Ethiopic Twitter Dataset for Abusive Speech in Amharic
• Distributionally Robust Removal of Malicious Nodes from Networks