# A Legal Approach to Hate Speech: Operationalizing the EU's Legal Framework against the Expression of Hatred as an NLP Task

arXiv: https://arxiv.org/abs/2004.03422 · HTML: https://mlsanity.com/paper/2004.03422/

## Key facts

- Cited by 11 papers in the map (data through 7 Oct 2026).
- First posted on arXiv 7 Apr 2020.

## Built on by (most cited first, 11 of 11)

- [Hate Speech Criteria: A Modular Approach to Task-Specific Hate Speech Definitions](https://mlsanity.com/paper/2206.15455/) (2022)
- [Explainable Abuse Detection as Intent Classification and Slot Filling](https://mlsanity.com/paper/2210.02659/) (2022)
- [HateModerate: Testing Hate Speech Detectors against Content Moderation Policies](https://mlsanity.com/paper/2307.12418/) (2023)
- [Towards Legally Enforceable Hate Speech Detection for Public Forums](https://mlsanity.com/paper/2305.13677/) (2023)
- [Exploring Boundaries and Intensities in Offensive and Hate Speech: Unveiling the Complex Spectrum of Social Media Discourse](https://mlsanity.com/paper/2404.12042/) (2024)
- [A Survey of Machine Learning Models and Datasets for the Multi-label Classification of Textual Hate Speech in English](https://mlsanity.com/paper/2504.08609/) (2025)
- [Conditioning Large Language Models on Legal Systems? Detecting Punishable Hate Speech](https://mlsanity.com/paper/2506.03009/) (2025)
- [Legal NLP Meets MiCAR: Advancing the Analysis of Crypto White Papers](https://mlsanity.com/paper/2310.10333/) (2023)
- [Improving Hate Speech Classification with Cross-Taxonomy Dataset Integration](https://mlsanity.com/paper/2503.05357/) (2025)
- [Evaluating Large Language Models for Antisemitic Incident Classification](https://mlsanity.com/paper/2607.04890/) (2026)
- [MUCnoHARM@GermEval Shared Task 2026: Retrieval-based In-Context Learning for Defamatory Offences, and Where It Falls Short](https://mlsanity.com/paper/2609.09791/) (2026)

## Abstract (arXiv)

We propose a 'legal approach' to hate speech detection by operationalization of the decision as to whether a post is subject to criminal law into an NLP task. Comparing existing regulatory regimes for hate speech, we base our investigation on the European Union's framework as it provides a widely applicable legal minimum standard. Accurately judging whether a post is punishable or not usually requires legal training. We show that, by breaking the legal assessment down into a series of simpler sub-decisions, even laypersons can annotate consistently. Based on a newly annotated dataset, our experiments show that directly learning an automated model of punishable content is challenging. However, learning the two sub-tasks of `target group' and `targeting conduct' instead of an end-to-end approach to punishability yields better results. Overall, our method also provides decisions that are more transparent than those of end-to-end models, which is a crucial point in legal decision-making.