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arXiv · 1706.01206

One-step and Two-step Classification for Abusive Language Detection on Twitter

Ji Ho Park·Pascale Fung
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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.

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Ji Ho Park, Pascale Fung. 2017-06-05. One-step and Two-step Classification for Abusive Language Detection on Twitter. https://arxiv.org/abs/1706.01206

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