ITU

Annotating Online Misogyny

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

View graph of relations

Online misogyny, a category of online abusive language, has serious and harmful social consequences. Automatic detection of misogynistic language online, while imperative, poses complicated challenges to both data gathering, data annotation, and bias mitigation, as this type of data is linguistically complex and diverse. This paper makes three contributions in this area: Firstly, we describe the detailed design of our iterative annotation process and codebook. Secondly, we present a comprehensive taxonomy of labels for annotating misogyny in natural written language, and finally, we introduce a high-quality dataset of annotated posts sampled from social media posts.
Original languageEnglish
Title of host publicationProceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)
PublisherAssociation for Computational Linguistics
Publication date3 Aug 2021
Pages3181–3197
Publication statusPublished - 3 Aug 2021
Event59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing -
Duration: 1 Aug 2021 → …

Conference

Conference59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing
Periode01/08/2021 → …

Bibliographical note

BFI:2

Downloads

No data available

ID: 86115827