@inproceedings{hada-etal-2021-ruddit,
title = "Ruddit: {N}orms of Offensiveness for {E}nglish {R}eddit Comments",
author = "Hada, Rishav and
Sudhir, Sohi and
Mishra, Pushkar and
Yannakoudakis, Helen and
Mohammad, Saif M. and
Shutova, Ekaterina",
editor = "Zong, Chengqing and
Xia, Fei and
Li, Wenjie and
Navigli, Roberto",
booktitle = "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 = aug,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.acl-long.210",
doi = "10.18653/v1/2021.acl-long.210",
pages = "2700--2717",
abstract = "On social media platforms, hateful and offensive language negatively impact the mental well-being of users and the participation of people from diverse backgrounds. Automatic methods to detect offensive language have largely relied on datasets with categorical labels. However, comments can vary in their degree of offensiveness. We create the first dataset of English language Reddit comments that has fine-grained, real-valued scores between -1 (maximally supportive) and 1 (maximally offensive). The dataset was annotated using Best{--}Worst Scaling, a form of comparative annotation that has been shown to alleviate known biases of using rating scales. We show that the method produces highly reliable offensiveness scores. Finally, we evaluate the ability of widely-used neural models to predict offensiveness scores on this new dataset.",
}
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%0 Conference Proceedings
%T Ruddit: Norms of Offensiveness for English Reddit Comments
%A Hada, Rishav
%A Sudhir, Sohi
%A Mishra, Pushkar
%A Yannakoudakis, Helen
%A Mohammad, Saif M.
%A Shutova, Ekaterina
%Y Zong, Chengqing
%Y Xia, Fei
%Y Li, Wenjie
%Y Navigli, Roberto
%S 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)
%D 2021
%8 August
%I Association for Computational Linguistics
%C Online
%F hada-etal-2021-ruddit
%X On social media platforms, hateful and offensive language negatively impact the mental well-being of users and the participation of people from diverse backgrounds. Automatic methods to detect offensive language have largely relied on datasets with categorical labels. However, comments can vary in their degree of offensiveness. We create the first dataset of English language Reddit comments that has fine-grained, real-valued scores between -1 (maximally supportive) and 1 (maximally offensive). The dataset was annotated using Best–Worst Scaling, a form of comparative annotation that has been shown to alleviate known biases of using rating scales. We show that the method produces highly reliable offensiveness scores. Finally, we evaluate the ability of widely-used neural models to predict offensiveness scores on this new dataset.
%R 10.18653/v1/2021.acl-long.210
%U https://aclanthology.org/2021.acl-long.210
%U https://doi.org/10.18653/v1/2021.acl-long.210
%P 2700-2717
Markdown (Informal)
[Ruddit: Norms of Offensiveness for English Reddit Comments](https://aclanthology.org/2021.acl-long.210) (Hada et al., ACL-IJCNLP 2021)
ACL
- Rishav Hada, Sohi Sudhir, Pushkar Mishra, Helen Yannakoudakis, Saif M. Mohammad, and Ekaterina Shutova. 2021. Ruddit: Norms of Offensiveness for English Reddit Comments. 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 2700–2717, Online. Association for Computational Linguistics.
