  The rise of online communication platforms has been accompanied by some
undesirable effects, such as the proliferation of aggressive and abusive
behaviour online. Aiming to tackle this problem, the natural language
processing (NLP) community has experimented with a range of techniques for
abuse detection. While achieving substantial success, these methods have so far
only focused on modelling the linguistic properties of the comments and the
online communities of users, disregarding the emotional state of the users and
how this might affect their language. The latter is, however, inextricably
linked to abusive behaviour. In this paper, we present the first joint model of
emotion and abusive language detection, experimenting in a multi-task learning
framework that allows one task to inform the other. Our results demonstrate
that incorporating affective features leads to significant improvements in
abuse detection performance across datasets.
