Predictive Embeddings for Hate Speech Detection on Twitter
Rohan Kshirsagar, Tyrus Cukuvac, Kathy McKeown, Susan McGregor
Abstract
We present a neural-network based approach to classifying online hate speech in general, as well as racist and sexist speech in particular. Using pre-trained word embeddings and max/mean pooling from simple, fully-connected transformations of these embeddings, we are able to predict the occurrence of hate speech on three commonly used publicly available datasets. Our models match or outperform state of the art F1 performance on all three datasets using significantly fewer parameters and minimal feature preprocessing compared to previous methods.- Anthology ID:
- W18-5104
- Volume:
- Proceedings of the 2nd Workshop on Abusive Language Online (ALW2)
- Month:
- October
- Year:
- 2018
- Address:
- Brussels, Belgium
- Editors:
- Darja Fišer, Ruihong Huang, Vinodkumar Prabhakaran, Rob Voigt, Zeerak Waseem, Jacqueline Wernimont
- Venue:
- ALW
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 26–32
- Language:
- URL:
- https://aclanthology.org/W18-5104
- DOI:
- 10.18653/v1/W18-5104
- Cite (ACL):
- Rohan Kshirsagar, Tyrus Cukuvac, Kathy McKeown, and Susan McGregor. 2018. Predictive Embeddings for Hate Speech Detection on Twitter. In Proceedings of the 2nd Workshop on Abusive Language Online (ALW2), pages 26–32, Brussels, Belgium. Association for Computational Linguistics.
- Cite (Informal):
- Predictive Embeddings for Hate Speech Detection on Twitter (Kshirsagar et al., ALW 2018)
- PDF:
- https://preview.aclanthology.org/naacl-24-ws-corrections/W18-5104.pdf