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
Bibkey:
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)
Copy Citation:
PDF:
https://preview.aclanthology.org/naacl-24-ws-corrections/W18-5104.pdf