mlsanityData to 14 Oct 2026

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

arXiv 2403.02268 ↗First posted 4 Mar 2024cs.CL cs.AI cs.CYcited by 9

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Abstract (arXiv)

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.

Key facts. Cited by 9 papers in the map (data through 14 Oct 2026). Builds on 11 of its 51 references that are in the map. First posted on arXiv 4 Mar 2024.

Builds on

11 of 51 references.

References · 11

  1. 2017Automated Hate Speech Detection and the Problem of Offensive Languagecited by 736
  2. 2020Learning from the Worst: Dynamically Generated Datasets to Improve Online Hate Detectioncited by 221
  3. 2021MultiVerS: Improving scientific claim verification with weak supervision and full-document contextcited by 143
  4. 2017Understanding Abuse: A Typology of Abusive Language Detection Subtaskscited by 116
  5. 2017Measuring the Reliability of Hate Speech Annotations: The Case of the European Refugee Crisiscited by 96
  6. 2023When the Majority is Wrong: Modeling Annotator Disagreement for Subjective Taskscited by 90
  7. 2021Human-in-the-Loop for Data Collection: a Multi-Target Counter Narrative Dataset to Fight Online Hate Speechcited by 82
  8. 2021Agreeing to Disagree: Annotating Offensive Language Datasets with Annotators' Disagreementcited by 72
  9. 2021ConvAbuse: Data, Analysis, and Benchmarks for Nuanced Abuse Detection in Conversational AIcited by 46
  10. 2021SWSR: A Chinese Dataset and Lexicon for Online Sexism Detectioncited by 42
  11. 2021Whose Opinions Matter? Perspective-aware Models to Identify Opinions of Hate Speech Victims in Abusive Language Detectioncited by 38

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 3; 2025: 3; 2026: 3.

9 papers.

  1. 2025Aligned Probing: Relating Toxic Behavior and Model Internalscited by 5
  2. 2024Adaptable Moral Stances of Large Language Models on Sexist Content: Implications for Society and Gender Discoursecited by 4
  3. 2024Designing NLP Systems That Adapt to Diverse Worldviewscited by 2
  4. 2024HateGPT: Unleashing GPT-3.5 Turbo to Combat Hate Speech on Xcited by 0
  5. 2025Tackling Social Bias against the Poor: A Dataset and Taxonomy on Aporophobiacited by 0
  6. 2025Web(er) of Hate: A Survey on How Hate Speech Is Typedcited by 0
  7. 2026IYKYK (But AI Doesn't): Automated Content Moderation Does Not Capture Communities' Heterogeneous Attitudes Towards Reclaimed Languagecited by 0
  8. 2026Sexualised synthetic personas encode and amplify gendered power asymmetries through voicecited by 0
  9. 2026Beyond a Global Norm: Personalizing Toxicity Sensitivity in Language Models Without Retrainingcited by 0