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.
@article{arxiv.1809.10644,
title = {Predictive Embeddings for Hate Speech Detection on Twitter},
author = {Rohan Kshirsagar and Tyus Cukuvac and Kathleen McKeown and Susan McGregor},
journal= {arXiv preprint arXiv:1809.10644},
year = {2018}
}
Comments
Accepted at Abusive Language Online Workshop, EMNLP 2018; 7 pages 7 figures