Annotating Online Misogyny

Philine Zeinert, Nanna Inie, Leon Derczynski


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.
Anthology ID:
2021.acl-long.247
Original:
2021.acl-long.247v1
Version 2:
2021.acl-long.247v2
Volume:
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)
Month:
August
Year:
2021
Address:
Online
Venues:
ACL | IJCNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
3181–3197
Language:
URL:
https://aclanthology.org/2021.acl-long.247
DOI:
10.18653/v1/2021.acl-long.247
Bibkey:
Cite (ACL):
Philine Zeinert, Nanna Inie, and Leon Derczynski. 2021. Annotating Online Misogyny. In 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), pages 3181–3197, Online. Association for Computational Linguistics.
Cite (Informal):
Annotating Online Misogyny (Zeinert et al., ACL-IJCNLP 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.acl-long.247.pdf
Video:
 https://www.youtube.com/watch?v=xayfVkt7gwo
Video:
 https://aclanthology.org/2021.acl-long.247.mp4
Code
 phze22/Online-Misogyny-in-Danish-Bajer
Data
bajer_danish_misogyny