Introducing CAD: the Contextual Abuse Dataset

Bertie Vidgen, Dong Nguyen, Helen Margetts, Patricia Rossini, Rebekah Tromble


Abstract
Online abuse can inflict harm on users and communities, making online spaces unsafe and toxic. Progress in automatically detecting and classifying abusive content is often held back by the lack of high quality and detailed datasets.We introduce a new dataset of primarily English Reddit entries which addresses several limitations of prior work. It (1) contains six conceptually distinct primary categories as well as secondary categories, (2) has labels annotated in the context of the conversation thread, (3) contains rationales and (4) uses an expert-driven group-adjudication process for high quality annotations. We report several baseline models to benchmark the work of future researchers. The annotated dataset, annotation guidelines, models and code are freely available.
Anthology ID:
2021.naacl-main.182
Volume:
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
Month:
June
Year:
2021
Address:
Online
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
2289–2303
Language:
URL:
https://aclanthology.org/2021.naacl-main.182
DOI:
10.18653/v1/2021.naacl-main.182
Bibkey:
Cite (ACL):
Bertie Vidgen, Dong Nguyen, Helen Margetts, Patricia Rossini, and Rebekah Tromble. 2021. Introducing CAD: the Contextual Abuse Dataset. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 2289–2303, Online. Association for Computational Linguistics.
Cite (Informal):
Introducing CAD: the Contextual Abuse Dataset (Vidgen et al., NAACL 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.naacl-main.182.pdf
Video:
 https://aclanthology.org/2021.naacl-main.182.mp4
Code
 dongpng/cad_naacl2021