Abstract

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.

Highlights

  • Aggressive and abusive behaviour online can lead to severe psychological consequences for its victims (Munro, 2011)

  • The natural language processing (NLP) community has experimented with a range of techniques for abuse detection, such as recurrent and convolutional neural networks (Pavlopoulos et al, 2017; Park and Fung, 2017; Wang, 2018), character-based models (Nobata et al, 2016) and graph-based learning methods (Mishra et al, 2018a; Aglionby et al, 2019; Mishra et al, 2019a), obtaining promising results

  • Our results demonstrate that the multitask learning (MTL) models significantly outperform single-task learning (STL) in two different abuse detection datasets

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Summary

Introduction

Aggressive and abusive behaviour online can lead to severe psychological consequences for its victims (Munro, 2011). This stresses the need for automated techniques for abusive language detection, a problem that has recently gained a great deal of interest in the natural language processing community. We propose to model these two phenomena jointly and present the first abusive language detection method that incorporates affective features via a multitask learning (MTL) paradigm

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