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

• URL: http://arxiv.org/abs/2004.03422v3
• Date: Tue, 5 Oct 2021 16:46:16 GMT
• Title: A Legal Approach to Hate Speech: Operationalizing the EU's Legal Framework against the Expression of Hatred as an NLP Task
• Authors: Frederike Zufall, Marius Hamacher, Katharina Kloppenborg, Torsten Zesch
• Abstract summary: We propose a 'legal approach' to hate speech detection by operationalization of the decision as to whether a post is subject to criminal law. We show that, by breaking the legal assessment down into a series of simpler sub-decisions, even laypersons can annotate consistently.
• Score: 2.248133901806859
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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