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    <abstract>Many discussions on online platforms suffer from users offending others by using abusive terminology, threatening each other, or being sarcastic. Since an automatic detection of abusive language can support human moderators of online discussion platforms, detecting abusiveness has recently received increased attention. However, the existing approaches simply train one classifier for the whole variety of abusiveness. In contrast, our approach is to distinguish explicitly abusive cases from the more “shadowed” ones. By dynamically extending a lexicon of abusive terms (e.g., including new obfuscations of abusive terms), our approach can support a moderator with explicit unraveled explanations for why something was flagged as abusive: due to known explicitly abusive terms, due to newly detected (obfuscated) terms, or due to shadowed cases.</abstract>
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%0 Conference Proceedings
%T Unraveling the Search Space of Abusive Language in Wikipedia with Dynamic Lexicon Acquisition
%A Chen, Wei-Fan
%A Al Khatib, Khalid
%A Hagen, Matthias
%A Wachsmuth, Henning
%A Stein, Benno
%Y Feldman, Anna
%Y Da San Martino, Giovanni
%Y Barrón-Cedeño, Alberto
%Y Brew, Chris
%Y Leberknight, Chris
%Y Nakov, Preslav
%S Proceedings of the Second Workshop on Natural Language Processing for Internet Freedom: Censorship, Disinformation, and Propaganda
%D 2019
%8 November
%I Association for Computational Linguistics
%C Hong Kong, China
%F chen-etal-2019-unraveling
%X Many discussions on online platforms suffer from users offending others by using abusive terminology, threatening each other, or being sarcastic. Since an automatic detection of abusive language can support human moderators of online discussion platforms, detecting abusiveness has recently received increased attention. However, the existing approaches simply train one classifier for the whole variety of abusiveness. In contrast, our approach is to distinguish explicitly abusive cases from the more “shadowed” ones. By dynamically extending a lexicon of abusive terms (e.g., including new obfuscations of abusive terms), our approach can support a moderator with explicit unraveled explanations for why something was flagged as abusive: due to known explicitly abusive terms, due to newly detected (obfuscated) terms, or due to shadowed cases.
%R 10.18653/v1/D19-5009
%U https://aclanthology.org/D19-5009
%U https://doi.org/10.18653/v1/D19-5009
%P 76-82

Markdown (Informal)

[Unraveling the Search Space of Abusive Language in Wikipedia with Dynamic Lexicon Acquisition](https://aclanthology.org/D19-5009) (Chen et al., NLP4IF 2019)

• Unraveling the Search Space of Abusive Language in Wikipedia with Dynamic Lexicon Acquisition (Chen et al., NLP4IF 2019)

ACL

• Wei-Fan Chen, Khalid Al Khatib, Matthias Hagen, Henning Wachsmuth, and Benno Stein. 2019. Unraveling the Search Space of Abusive Language in Wikipedia with Dynamic Lexicon Acquisition. In Proceedings of the Second Workshop on Natural Language Processing for Internet Freedom: Censorship, Disinformation, and Propaganda, pages 76–82, Hong Kong, China. Association for Computational Linguistics.