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We propose a 'legal approach' to hate speech detection by operationalizationof the decision as to whether a post is subject to criminal law into an NLPtask. Comparing existing regulatory regimes for hate speech, we base ourinvestigation on the European Union's framework as it provides a widelyapplicable legal minimum standard. Accurately judging whether a post ispunishable or not usually requires legal training. We show that, by breakingthe legal assessment down into a series of simpler sub-decisions, evenlaypersons can annotate consistently. Based on a newly annotated dataset, ourexperiments show that directly learning an automated model of punishablecontent is challenging. However, learning the two sub-tasks of `target group'and `targeting conduct' instead of an end-to-end approach to punishabilityyields better results. Overall, our method also provides decisions that aremore transparent than those of end-to-end models, which is a crucial point inlegal decision-making.

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