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        <title>One-step and Two-step Classification for Abusive Language Detection on Twitter</title>
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            <title>Proceedings of the First Workshop on Abusive Language Online</title>
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            <namePart type="given">Zeerak</namePart>
            <namePart type="family">Waseem</namePart>
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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.</abstract>
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%0 Conference Proceedings
%T One-step and Two-step Classification for Abusive Language Detection on Twitter
%A Park, Ji Ho
%A Fung, Pascale
%Y Waseem, Zeerak
%Y Chung, Wendy Hui Kyong
%Y Hovy, Dirk
%Y Tetreault, Joel
%S Proceedings of the First Workshop on Abusive Language Online
%D 2017
%8 August
%I Association for Computational Linguistics
%C Vancouver, BC, Canada
%F park-fung-2017-one
%X 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.
%R 10.18653/v1/W17-3006
%U https://aclanthology.org/W17-3006
%U https://doi.org/10.18653/v1/W17-3006
%P 41-45

Markdown (Informal)

[One-step and Two-step Classification for Abusive Language Detection on Twitter](https://aclanthology.org/W17-3006) (Park & Fung, ALW 2017)

• One-step and Two-step Classification for Abusive Language Detection on Twitter (Park & Fung, ALW 2017)

ACL

• Ji Ho Park and Pascale Fung. 2017. One-step and Two-step Classification for Abusive Language Detection on Twitter. In Proceedings of the First Workshop on Abusive Language Online, pages 41–45, Vancouver, BC, Canada. Association for Computational Linguistics.