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
- 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)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/N18-2019.pdf