Automatic abusive language detection is a difficult but important task for online social media. Our research explores a two-step approach of performing classification on abusive language and then classifying into specific types and compares it with one-step approach of doing one multi-class classification for detecting sexist and racist languages. With a public English Twitter corpus of 20 thousand tweets in the type of sexism and racism, our approach shows a promising performance of 0.827 F-measure by using HybridCNN in one-step and 0.824 F-measure by using logistic regression in two-steps.
@article{arxiv.1706.01206,
title = {One-step and Two-step Classification for Abusive Language Detection on Twitter},
author = {Ji Ho Park and Pascale Fung},
journal= {arXiv preprint arXiv:1706.01206},
year = {2017}
}
Comments
ALW1: 1st Workshop on Abusive Language Online to be held at the annual meeting of the Association of Computational Linguistics (ACL) 2017 (Vancouver, Canada), August 4th, 2017