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

arXiv: https://arxiv.org/abs/2403.02268 · HTML: https://mlsanity.com/paper/2403.02268/

## 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 (most cited first, 11 of 11)

- [Automated Hate Speech Detection and the Problem of Offensive Language](https://mlsanity.com/paper/1703.04009/) (2017)
- [Learning from the Worst: Dynamically Generated Datasets to Improve Online Hate Detection](https://mlsanity.com/paper/2012.15761/) (2020)
- [MultiVerS: Improving scientific claim verification with weak supervision and full-document context](https://mlsanity.com/paper/2112.01640/) (2021)
- [Understanding Abuse: A Typology of Abusive Language Detection Subtasks](https://mlsanity.com/paper/1705.09899/) (2017)
- [Measuring the Reliability of Hate Speech Annotations: The Case of the European Refugee Crisis](https://mlsanity.com/paper/1701.08118/) (2017)
- [When the Majority is Wrong: Modeling Annotator Disagreement for Subjective Tasks](https://mlsanity.com/paper/2305.06626/) (2023)
- [Human-in-the-Loop for Data Collection: a Multi-Target Counter Narrative Dataset to Fight Online Hate Speech](https://mlsanity.com/paper/2107.08720/) (2021)
- [Agreeing to Disagree: Annotating Offensive Language Datasets with Annotators' Disagreement](https://mlsanity.com/paper/2109.13563/) (2021)
- [ConvAbuse: Data, Analysis, and Benchmarks for Nuanced Abuse Detection in Conversational AI](https://mlsanity.com/paper/2109.09483/) (2021)
- [SWSR: A Chinese Dataset and Lexicon for Online Sexism Detection](https://mlsanity.com/paper/2108.03070/) (2021)
- [Whose Opinions Matter? Perspective-aware Models to Identify Opinions of Hate Speech Victims in Abusive Language Detection](https://mlsanity.com/paper/2106.15896/) (2021)

## Built on by (most cited first, 9 of 9)

- [Aligned Probing: Relating Toxic Behavior and Model Internals](https://mlsanity.com/paper/2503.13390/) (2025)
- [Adaptable Moral Stances of Large Language Models on Sexist Content: Implications for Society and Gender Discourse](https://mlsanity.com/paper/2410.00175/) (2024)
- [Designing NLP Systems That Adapt to Diverse Worldviews](https://mlsanity.com/paper/2405.11197/) (2024)
- [HateGPT: Unleashing GPT-3.5 Turbo to Combat Hate Speech on X](https://mlsanity.com/paper/2411.09214/) (2024)
- [Tackling Social Bias against the Poor: A Dataset and Taxonomy on Aporophobia](https://mlsanity.com/paper/2504.13085/) (2025)
- [Web(er) of Hate: A Survey on How Hate Speech Is Typed](https://mlsanity.com/paper/2506.16190/) (2025)
- [IYKYK (But AI Doesn't): Automated Content Moderation Does Not Capture Communities' Heterogeneous Attitudes Towards Reclaimed Language](https://mlsanity.com/paper/2604.16654/) (2026)
- [Sexualised synthetic personas encode and amplify gendered power asymmetries through voice](https://mlsanity.com/paper/2606.21366/) (2026)
- [Beyond a Global Norm: Personalizing Toxicity Sensitivity in Language Models Without Retraining](https://mlsanity.com/paper/2607.23175/) (2026)

## 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.