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Automatic hate speech detection is an important yet complex task, requiringknowledge of common sense, stereotypes of protected groups, and histories ofdiscrimination, each of which may constantly evolve. In this paper, we proposea group-specific approach to NLP for online hate speech detection. The approachconsists of creating and infusing historical and linguistic knowledge about aparticular protected group into hate speech detection models, analyzinghistorical data about discrimination against a protected group to betterpredict spikes in hate speech against that group, and critically evaluatinghate speech detection models through lenses of intersectionality and ethics. Wedemonstrate this approach through a case study on NLP for detection ofantisemitic hate speech. The case study synthesizes the currentEnglish-language literature on NLP for antisemitism detection, introduces anovel knowledge graph of antisemitic history and language from the 20th centuryto the present, infuses information from the knowledge graph into a set oftweets over Logistic Regression and uncased DistilBERT baselines, and suggeststhat incorporating context from the knowledge graph can help models pick upsubtle stereotypes.

A Group-Specific Approach to NLP for Hate Speech Detection