Subjective Isms? On the Danger of Conflating Hate and Offense in Abusive Language Detection

Anonymous

TL;DR: Modelling hate speech as offensiveness is wrong.

Abstract:

Natural language processing (NLP) 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, offense.

Paper Type: short

Research Area: Ethics, Bias, and Fairness

Contribution Types: Position papers, Theory

Languages Studied: English