ITU

Offensive Language and Hate Speech Detection for Danish

Research output: Conference Article in Proceeding or Book/Report chapterArticle in proceedingsResearchpeer-review

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Offensive Language and Hate Speech Detection for Danish. / Sigurbergsson, Guðbjartur; Derczynski, Leon.

Proceedings of the International Conference on Language Resources and Evaluation: LREC 2020. European Language Resources Association, 2020. p. 3498–3508.

Research output: Conference Article in Proceeding or Book/Report chapterArticle in proceedingsResearchpeer-review

Harvard

Sigurbergsson, G & Derczynski, L 2020, Offensive Language and Hate Speech Detection for Danish. in Proceedings of the International Conference on Language Resources and Evaluation: LREC 2020. European Language Resources Association, pp. 3498–3508. <http://www.derczynski.com/papers/danish_hsd.pdf>

APA

Sigurbergsson, G., & Derczynski, L. (2020). Offensive Language and Hate Speech Detection for Danish. In Proceedings of the International Conference on Language Resources and Evaluation: LREC 2020 (pp. 3498–3508). European Language Resources Association. http://www.derczynski.com/papers/danish_hsd.pdf

Vancouver

Sigurbergsson G, Derczynski L. Offensive Language and Hate Speech Detection for Danish. In Proceedings of the International Conference on Language Resources and Evaluation: LREC 2020. European Language Resources Association. 2020. p. 3498–3508

Author

Sigurbergsson, Guðbjartur ; Derczynski, Leon. / Offensive Language and Hate Speech Detection for Danish. Proceedings of the International Conference on Language Resources and Evaluation: LREC 2020. European Language Resources Association, 2020. pp. 3498–3508

Bibtex

@inproceedings{bb9b25ce3bbf4418b3e31c086d105ff0,
title = "Offensive Language and Hate Speech Detection for Danish",
abstract = "The presence of offensive language on social media platforms and the implications this poses is becoming a major concern in modernsociety. Given the enormous amount of content created every day, automatic methods are required to detect and deal with this typeof content. Until now, most of the research has focused on solving the problem for the English language, while the problem ismultilingual. We construct a Danish dataset DKHATE containing user-generated comments from various social media platforms,and to our knowledge, the first of its kind, annotated for various types and target of offensive language. We develop four automaticclassification systems, each designed to work for both the English and the Danish language. In the detection of offensive language inEnglish, the best performing system achieves a macro averaged F1-score of 0:74, and the best performing system for Danish achieves a macro averaged F1-score of 0:70. In the detection of whether or not an offensive post is targeted, the best performing system for English achieves a macro averaged F1-score of 0:62, while the best performing system for Danish achieves a macro averaged F1-score of 0:73. Finally, in the detection of the target type in a targeted offensive post, the best performing system for English achieves a macro averaged F1-score of 0:56, and the best performing system for Danish achieves a macro averaged F1-score of 0:63. Our work for both the English and the Danish language captures the type and targets of offensive language, and present automatic methods for detecting different kinds of offensive language such as hate speech and cyberbullying.",
author = "Gu{\dh}bjartur Sigurbergsson and Leon Derczynski",
year = "2020",
month = may,
day = "1",
language = "English",
pages = "3498–3508",
booktitle = "Proceedings of the International Conference on Language Resources and Evaluation",
publisher = "European Language Resources Association",

}

RIS

TY - GEN

T1 - Offensive Language and Hate Speech Detection for Danish

AU - Sigurbergsson, Guðbjartur

AU - Derczynski, Leon

PY - 2020/5/1

Y1 - 2020/5/1

N2 - The presence of offensive language on social media platforms and the implications this poses is becoming a major concern in modernsociety. Given the enormous amount of content created every day, automatic methods are required to detect and deal with this typeof content. Until now, most of the research has focused on solving the problem for the English language, while the problem ismultilingual. We construct a Danish dataset DKHATE containing user-generated comments from various social media platforms,and to our knowledge, the first of its kind, annotated for various types and target of offensive language. We develop four automaticclassification systems, each designed to work for both the English and the Danish language. In the detection of offensive language inEnglish, the best performing system achieves a macro averaged F1-score of 0:74, and the best performing system for Danish achieves a macro averaged F1-score of 0:70. In the detection of whether or not an offensive post is targeted, the best performing system for English achieves a macro averaged F1-score of 0:62, while the best performing system for Danish achieves a macro averaged F1-score of 0:73. Finally, in the detection of the target type in a targeted offensive post, the best performing system for English achieves a macro averaged F1-score of 0:56, and the best performing system for Danish achieves a macro averaged F1-score of 0:63. Our work for both the English and the Danish language captures the type and targets of offensive language, and present automatic methods for detecting different kinds of offensive language such as hate speech and cyberbullying.

AB - The presence of offensive language on social media platforms and the implications this poses is becoming a major concern in modernsociety. Given the enormous amount of content created every day, automatic methods are required to detect and deal with this typeof content. Until now, most of the research has focused on solving the problem for the English language, while the problem ismultilingual. We construct a Danish dataset DKHATE containing user-generated comments from various social media platforms,and to our knowledge, the first of its kind, annotated for various types and target of offensive language. We develop four automaticclassification systems, each designed to work for both the English and the Danish language. In the detection of offensive language inEnglish, the best performing system achieves a macro averaged F1-score of 0:74, and the best performing system for Danish achieves a macro averaged F1-score of 0:70. In the detection of whether or not an offensive post is targeted, the best performing system for English achieves a macro averaged F1-score of 0:62, while the best performing system for Danish achieves a macro averaged F1-score of 0:73. Finally, in the detection of the target type in a targeted offensive post, the best performing system for English achieves a macro averaged F1-score of 0:56, and the best performing system for Danish achieves a macro averaged F1-score of 0:63. Our work for both the English and the Danish language captures the type and targets of offensive language, and present automatic methods for detecting different kinds of offensive language such as hate speech and cyberbullying.

M3 - Article in proceedings

SP - 3498

EP - 3508

BT - Proceedings of the International Conference on Language Resources and Evaluation

PB - European Language Resources Association

ER -

ID: 84743801