Abstract
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 language | English |
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| Title of host publication | Proceedings 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) |
| Publisher | Association for Computational Linguistics |
| Publication date | 3 Aug 2021 |
| Pages | 3181–3197 |
| Publication status | Published - 3 Aug 2021 |
| Event | 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing - Duration: 1 Aug 2021 → … |
Conference
| Conference | 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing |
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| Period | 01/08/2021 → … |
Keywords
- Online Misogyny
- Automatic Detection
- Data Annotation
- Bias Mitigation
- Social Media Analysis