Subjective Isms? On the Danger of Conflating Hate and Offence in Abusive Language Detection

2024/03/04 by Amanda Cercas Curry, Gavin Abercrombie, Curry, Amanda Cercas +3 · 3 citations
Computer Science · #Artificial Intelligence (cs.AI) #Computation and Language (cs.CL) #Computers and Society (cs.CY) #FOS: Computer and information sciences #Hate Speech and Cyberbullying Detection

paper · pdf · doi:10.48550/arxiv.2403.02268

openalex publication_date 2024/03/04 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/03

Abstract

Natural language processing research has begun to embrace the notion of annotator subjectivity, motivated by variations in labelling. This approach understands each annotator's view as valid, which can be highly suitable for tasks that embed subjectivity, e.g., sentiment analysis. However, this construction may be inappropriate for tasks such as hate speech detection, as it affords equal validity to all positions on e.g., sexism or racism. We argue that the conflation of hate and offence can invalidate findings on hate speech, and call for future work to be situated in theory, disentangling hate from its orthogonal concept, offence.

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• Beyond a Global Norm: Personalizing Toxicity Sensitivity in Language Models Without Retraining
• Sexualised synthetic personas encode and amplify gendered power asymmetries through voice
• Web(er) of Hate: A Survey on How Hate Speech Is Typed

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