Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

5-2021

Abstract

Automated hate speech detection in social media is a challenging task that has recently gained significant traction in the data mining and Natural Language Processing community. However, most of the existing methods adopt a supervised approach that depended heavily on the annotated hate speech datasets, which are imbalanced and often lack training samples for hateful content. This paper addresses the research gaps by proposing a novel multitask learning-based model, AngryBERT, which jointly learns hate speech detection with sentiment classification and target identification as secondary relevant tasks. We conduct extensive experiments to augment three commonly-used hate speech detection datasets. Our experiment results show that AngryBERT outperforms state-of-the-art single-task-learning and multitask learning baselines. We conduct ablation studies and case studies to empirically examine the strengths and characteristics of our AngryBERT model and show that the secondary tasks are able to improve hate speech detection.

Keywords

Hate speech detection, Social media, Multitask learning

Discipline

Databases and Information Systems | Social Media

Publication

Proceedings of the 25th Pacific-Asia Conference, PAKDD 2021, Virtual Conference, 2021 May 11-14

First Page

701

Last Page

713

ISBN

9783030757618

Identifier

10.1007/978-3-030-75762-5_55

Publisher

Springer

City or Country

Cham

Additional URL

http://doi.org/10.1007/978-3-030-75762-5_55

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