  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.
