One-step and Two-step Classification for Abusive Language Detection on Twitter
This work addresses the problem of detecting abusive language, specifically sexism and racism, for social media platforms, but it is incremental as it compares existing methods on a new dataset.
The paper tackled abusive language detection on Twitter by comparing a one-step multi-class classification approach with a two-step method, achieving F-measure scores of 0.827 and 0.824 respectively on a dataset of 20,000 tweets.
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.