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Title
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Improving Cross-Domain Hate Speech Detection by Reducing the False Positive Rate
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Author
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Abstract
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| Hate speech detection is an actively growing field of research with a variety of recently proposed approaches that allowed to push the state-of-the-art results. One of the challenges of such automated approaches – namely recent deep learning models – is a risk of false positives (i.e., false accusations), which may lead to over-blocking or removal of harmless social media content in applications with little moderator intervention. We evaluate deep learning models both under in-domain and cross-domain hate speech detection conditions, and introduce an SVM approach that allows to significantly improve the state-of-the-art results when combined with the deep learning models through a simple majority-voting ensemble. The improvement is mainly due to a reduction of the false positive rate. |
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Language
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English
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Source (book)
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Proceedings of the Fourth Workshop on NLP for Internet Freedom: Censorship, Disinformation, and Propaganda
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Publication
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Association for Computational Linguistics
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2021
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ISBN
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978-1-954085-26-8
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DOI
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10.18653/V1/2021.NLP4IF-1.3
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Volume/pages
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p. 17-22
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Medium
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E-only publicatie
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Full text (Publisher's DOI)
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