@inproceedings{guest-etal-2021-expert,
title = "An Expert Annotated Dataset for the Detection of Online Misogyny",
author = "Guest, Ella and
Vidgen, Bertie and
Mittos, Alexandros and
Sastry, Nishanth and
Tyson, Gareth and
Margetts, Helen",
editor = "Merlo, Paola and
Tiedemann, Jorg and
Tsarfaty, Reut",
booktitle = "Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume",
month = apr,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.eacl-main.114",
doi = "10.18653/v1/2021.eacl-main.114",
pages = "1336--1350",
abstract = "Online misogyny is a pernicious social problem that risks making online platforms toxic and unwelcoming to women. We present a new hierarchical taxonomy for online misogyny, as well as an expert labelled dataset to enable automatic classification of misogynistic content. The dataset consists of 6567 labels for Reddit posts and comments. As previous research has found untrained crowdsourced annotators struggle with identifying misogyny, we hired and trained annotators and provided them with robust annotation guidelines. We report baseline classification performance on the binary classification task, achieving accuracy of 0.93 and F1 of 0.43. The codebook and datasets are made freely available for future researchers.",
}
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<abstract>Online misogyny is a pernicious social problem that risks making online platforms toxic and unwelcoming to women. We present a new hierarchical taxonomy for online misogyny, as well as an expert labelled dataset to enable automatic classification of misogynistic content. The dataset consists of 6567 labels for Reddit posts and comments. As previous research has found untrained crowdsourced annotators struggle with identifying misogyny, we hired and trained annotators and provided them with robust annotation guidelines. We report baseline classification performance on the binary classification task, achieving accuracy of 0.93 and F1 of 0.43. The codebook and datasets are made freely available for future researchers.</abstract>
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%0 Conference Proceedings
%T An Expert Annotated Dataset for the Detection of Online Misogyny
%A Guest, Ella
%A Vidgen, Bertie
%A Mittos, Alexandros
%A Sastry, Nishanth
%A Tyson, Gareth
%A Margetts, Helen
%Y Merlo, Paola
%Y Tiedemann, Jorg
%Y Tsarfaty, Reut
%S Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume
%D 2021
%8 April
%I Association for Computational Linguistics
%C Online
%F guest-etal-2021-expert
%X Online misogyny is a pernicious social problem that risks making online platforms toxic and unwelcoming to women. We present a new hierarchical taxonomy for online misogyny, as well as an expert labelled dataset to enable automatic classification of misogynistic content. The dataset consists of 6567 labels for Reddit posts and comments. As previous research has found untrained crowdsourced annotators struggle with identifying misogyny, we hired and trained annotators and provided them with robust annotation guidelines. We report baseline classification performance on the binary classification task, achieving accuracy of 0.93 and F1 of 0.43. The codebook and datasets are made freely available for future researchers.
%R 10.18653/v1/2021.eacl-main.114
%U https://aclanthology.org/2021.eacl-main.114
%U https://doi.org/10.18653/v1/2021.eacl-main.114
%P 1336-1350
Markdown (Informal)
[An Expert Annotated Dataset for the Detection of Online Misogyny](https://aclanthology.org/2021.eacl-main.114) (Guest et al., EACL 2021)
ACL
- Ella Guest, Bertie Vidgen, Alexandros Mittos, Nishanth Sastry, Gareth Tyson, and Helen Margetts. 2021. An Expert Annotated Dataset for the Detection of Online Misogyny. In Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pages 1336–1350, Online. Association for Computational Linguistics.