Abusive Language Detection with Graph Convolutional Networks
Pushkar Mishra, Marco Del Tredici, Helen Yannakoudakis, Ekaterina Shutova
Abstract
Abuse on the Internet represents a significant societal problem of our time. Previous research on automated abusive language detection in Twitter has shown that community-based profiling of users is a promising technique for this task. However, existing approaches only capture shallow properties of online communities by modeling follower–following relationships. In contrast, working with graph convolutional networks (GCNs), we present the first approach that captures not only the structure of online communities but also the linguistic behavior of the users within them. We show that such a heterogeneous graph-structured modeling of communities significantly advances the current state of the art in abusive language detection.- Anthology ID:
- N19-1221
- Volume:
- Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)
- Month:
- June
- Year:
- 2019
- Address:
- Minneapolis, Minnesota
- Editors:
- Jill Burstein, Christy Doran, Thamar Solorio
- Venue:
- NAACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 2145–2150
- Language:
- URL:
- https://aclanthology.org/N19-1221
- DOI:
- 10.18653/v1/N19-1221
- Cite (ACL):
- Pushkar Mishra, Marco Del Tredici, Helen Yannakoudakis, and Ekaterina Shutova. 2019. Abusive Language Detection with Graph Convolutional Networks. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pages 2145–2150, Minneapolis, Minnesota. Association for Computational Linguistics.
- Cite (Informal):
- Abusive Language Detection with Graph Convolutional Networks (Mishra et al., NAACL 2019)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-5/N19-1221.pdf