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        <title>Directions for NLP Practices Applied to Online Hate Speech Detection</title>
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        <namePart type="given">Paula</namePart>
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    <name type="personal">
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    <name type="personal">
        <namePart type="given">Zeerak</namePart>
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            <namePart type="given">Zornitsa</namePart>
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            <namePart type="given">Yue</namePart>
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    <abstract>Addressing hate speech in online spaces has been conceptualized as a classification task that uses Natural Language Processing (NLP) techniques. Through this conceptualization, the hate speech detection task has relied on common conventions and practices from NLP. For instance, inter-annotator agreement is conceptualized as a way to measure dataset quality and certain metrics and benchmarks are used to assure model generalization. However, hate speech is a deeply complex and situated concept that eludes such static and disembodied practices. In this position paper, we critically reflect on these methodologies for hate speech detection, we argue that many conventions in NLP are poorly suited for the problem and encourage researchers to develop methods that are more appropriate for the task.</abstract>
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    <identifier type="doi">10.18653/v1/2022.emnlp-main.809</identifier>
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%0 Conference Proceedings
%T Directions for NLP Practices Applied to Online Hate Speech Detection
%A Fortuna, Paula
%A Dominguez, Monica
%A Wanner, Leo
%A Talat, Zeerak
%Y Goldberg, Yoav
%Y Kozareva, Zornitsa
%Y Zhang, Yue
%S Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, United Arab Emirates
%F fortuna-etal-2022-directions
%X Addressing hate speech in online spaces has been conceptualized as a classification task that uses Natural Language Processing (NLP) techniques. Through this conceptualization, the hate speech detection task has relied on common conventions and practices from NLP. For instance, inter-annotator agreement is conceptualized as a way to measure dataset quality and certain metrics and benchmarks are used to assure model generalization. However, hate speech is a deeply complex and situated concept that eludes such static and disembodied practices. In this position paper, we critically reflect on these methodologies for hate speech detection, we argue that many conventions in NLP are poorly suited for the problem and encourage researchers to develop methods that are more appropriate for the task.
%R 10.18653/v1/2022.emnlp-main.809
%U https://aclanthology.org/2022.emnlp-main.809
%U https://doi.org/10.18653/v1/2022.emnlp-main.809
%P 11794-11805

Markdown (Informal)

[Directions for NLP Practices Applied to Online Hate Speech Detection](https://aclanthology.org/2022.emnlp-main.809) (Fortuna et al., EMNLP 2022)

• Directions for NLP Practices Applied to Online Hate Speech Detection (Fortuna et al., EMNLP 2022)

ACL

• Paula Fortuna, Monica Dominguez, Leo Wanner, and Zeerak Talat. 2022. Directions for NLP Practices Applied to Online Hate Speech Detection. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 11794–11805, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.