Directions for NLP practices applied to online hate speech detection

Paula Fortuna; Mónica Domínguez; Leo Wanner; Zeerak Talat

doi:10.18653/v1/2022.emnlp-main.809

Directions for NLP practices applied to online hate speech detection

Paula Fortuna, Mónica Domínguez, Leo Wanner, Zeerak Talat

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract / Description of output

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.

Original language	English
Title of host publication	Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
Editors	Yoav Goldberg, Zornitsa Kozareva, Yue Zhang
Publisher	Association for Computational Linguistics
Pages	11794-11805
Number of pages	12
ISBN (Electronic)	9781959429401
DOIs	https://doi.org/10.18653/v1/2022.emnlp-main.809
Publication status	Published - 11 Dec 2022
Event	The 2022 Conference on Empirical Methods in Natural Language Processing - Abu Dhabi National Exhibition Centre, Abu Dhabi, United Arab Emirates Duration: 7 Dec 2022 → 11 Dec 2022 Conference number: 27 https://2022.emnlp.org/

Conference

Conference	The 2022 Conference on Empirical Methods in Natural Language Processing
Abbreviated title	EMNLP 2022
Country/Territory	United Arab Emirates
City	Abu Dhabi
Period	7/12/22 → 11/12/22
Internet address	https://2022.emnlp.org/

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10.18653/v1/2022.emnlp-main.809Licence: Creative Commons: Attribution (CC-BY)

FortunaEtalEMNLP2022DirectionsforNLPPracticesFinal published version, 167 KBLicence: Creative Commons: Attribution (CC-BY)

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Fortuna, P., Domínguez, M., Wanner, L., & Talat, Z. (2022). Directions for NLP practices applied to online hate speech detection. In Y. Goldberg, Z. Kozareva, & Y. Zhang (Eds.), Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing (pp. 11794-11805). Association for Computational Linguistics. https://doi.org/10.18653/v1/2022.emnlp-main.809

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title = "Directions for NLP practices applied to online hate speech detection",

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.",

author = "Paula Fortuna and M{\'o}nica Dom{\'i}nguez and Leo Wanner and Zeerak Talat",

note = "Publisher Copyright: {\textcopyright} 2022 Association for Computational Linguistics.; The 2022 Conference on Empirical Methods in Natural Language Processing, EMNLP 2022 ; Conference date: 07-12-2022 Through 11-12-2022",

year = "2022",

month = dec,

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doi = "10.18653/v1/2022.emnlp-main.809",

language = "English",

pages = "11794--11805",

editor = "Yoav Goldberg and Zornitsa Kozareva and Yue Zhang",

booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",

publisher = "Association for Computational Linguistics",

url = "https://2022.emnlp.org/",

}

Fortuna, P, Domínguez, M, Wanner, L & Talat, Z 2022, Directions for NLP practices applied to online hate speech detection. in Y Goldberg, Z Kozareva & Y Zhang (eds), Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, pp. 11794-11805, The 2022 Conference on Empirical Methods in Natural Language Processing, Abu Dhabi, United Arab Emirates, 7/12/22. https://doi.org/10.18653/v1/2022.emnlp-main.809

Directions for NLP practices applied to online hate speech detection. / Fortuna, Paula; Domínguez, Mónica; Wanner, Leo et al.
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing. ed. / Yoav Goldberg; Zornitsa Kozareva; Yue Zhang. Association for Computational Linguistics, 2022. p. 11794-11805.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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T1 - Directions for NLP practices applied to online hate speech detection

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AU - Domínguez, Mónica

AU - Wanner, Leo

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N2 - 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.

AB - 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.

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BT - Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

A2 - Goldberg, Yoav

A2 - Kozareva, Zornitsa

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T2 - The 2022 Conference on Empirical Methods in Natural Language Processing

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Directions for NLP practices applied to online hate speech detection

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