Leveraging Intra-User and Inter-User Representation Learning for Automated Hate Speech Detection

Jing Qian, Mai ElSherief, Elizabeth Belding, William Yang Wang

Abstract
Hate speech detection is a critical, yet challenging problem in Natural Language Processing (NLP). Despite the existence of numerous studies dedicated to the development of NLP hate speech detection approaches, the accuracy is still poor. The central problem is that social media posts are short and noisy, and most existing hate speech detection solutions take each post as an isolated input instance, which is likely to yield high false positive and negative rates. In this paper, we radically improve automated hate speech detection by presenting a novel model that leverages intra-user and inter-user representation learning for robust hate speech detection on Twitter. In addition to the target Tweet, we collect and analyze the user’s historical posts to model intra-user Tweet representations. To suppress the noise in a single Tweet, we also model the similar Tweets posted by all other users with reinforced inter-user representation learning techniques. Experimentally, we show that leveraging these two representations can significantly improve the f-score of a strong bidirectional LSTM baseline model by 10.1%.
Anthology ID:
N18-2019
Volume:
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)
Month:
June
Year:
2018
Address:
New Orleans, Louisiana
Editors:
Marilyn Walker, Heng Ji, Amanda Stent
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
118–123
Language:
URL:
https://aclanthology.org/N18-2019
DOI:
10.18653/v1/N18-2019
Bibkey:
Cite (ACL):
Jing Qian, Mai ElSherief, Elizabeth Belding, and William Yang Wang. 2018. Leveraging Intra-User and Inter-User Representation Learning for Automated Hate Speech Detection. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers), pages 118–123, New Orleans, Louisiana. Association for Computational Linguistics.
Cite (Informal):
Leveraging Intra-User and Inter-User Representation Learning for Automated Hate Speech Detection (Qian et al., NAACL 2018)
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PDF:
https://preview.aclanthology.org/nschneid-patch-4/N18-2019.pdf
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