Deep Learning for Hate Speech Detection in Tweets
Computation and Language
2017-06-02 v1 Information Retrieval
Abstract
Hate speech detection on Twitter is critical for applications like controversial event extraction, building AI chatterbots, content recommendation, and sentiment analysis. We define this task as being able to classify a tweet as racist, sexist or neither. The complexity of the natural language constructs makes this task very challenging. We perform extensive experiments with multiple deep learning architectures to learn semantic word embeddings to handle this complexity. Our experiments on a benchmark dataset of 16K annotated tweets show that such deep learning methods outperform state-of-the-art char/word n-gram methods by ~18 F1 points.
Cite
@article{arxiv.1706.00188,
title = {Deep Learning for Hate Speech Detection in Tweets},
author = {Pinkesh Badjatiya and Shashank Gupta and Manish Gupta and Vasudeva Varma},
journal= {arXiv preprint arXiv:1706.00188},
year = {2017}
}
Comments
In Proceedings of ACM WWW'17 Companion, Perth, Western Australia, Apr 2017 (WWW'17), 2 pages