One-step and Two-step Classification for Abusive Language Detection on Twitter
TL
TL;DR
This 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.
Abstract
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.
References18 items
1
Deep Learning for Hate Speech Detection in Tweets
2
A Survey on Hate Speech Detection using Natural Language Processing
3
Are You a Racist or Am I Seeing Things? Annotator Influence on Hate Speech Detection on Twitter
4
Measuring the Reliability of Hate Speech Annotations: The Case of the European Refugee Crisis
5