Subjective $\textit{Isms}$? On the Danger of Conflating Hate and Offence
  in Abusive Language Detection
Amanda Cercas Curry Gavin Abercrombie Zeerak Talat
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.