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Offensive Language and Hate Speech Detection for Danish

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The presence of offensive language on social media platforms and the implications this poses is becoming a major concern in modern
society. Given the enormous amount of content created every day, automatic methods are required to detect and deal with this type
of content. Until now, most of the research has focused on solving the problem for the English language, while the problem is
multilingual. 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 automatic
classification systems, each designed to work for both the English and the Danish language. In the detection of offensive language in
English, 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.
Original languageEnglish
Title of host publicationProceedings of the International Conference on Language Resources and Evaluation : LREC 2020
PublisherEuropean Language Resources Association
Publication date1 May 2020
Pages3498–3508
ISBN (Electronic)979-10-95546-34-4
Publication statusPublished - 1 May 2020

ID: 84743801