Understanding Abuse: A Typology of Abusive Language Detection Subtasks

Zeerak Waseem, Thomas Davidson, Dana Warmsley, Ingmar Weber


Abstract
As the body of research on abusive language detection and analysis grows, there is a need for critical consideration of the relationships between different subtasks that have been grouped under this label. Based on work on hate speech, cyberbullying, and online abuse we propose a typology that captures central similarities and differences between subtasks and discuss the implications of this for data annotation and feature construction. We emphasize the practical actions that can be taken by researchers to best approach their abusive language detection subtask of interest.
Anthology ID:
W17-3012
Volume:
Proceedings of the First Workshop on Abusive Language Online
Month:
August
Year:
2017
Address:
Vancouver, BC, Canada
Venue:
ALW
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
78–84
Language:
URL:
https://aclanthology.org/W17-3012
DOI:
10.18653/v1/W17-3012
Bibkey:
Cite (ACL):
Zeerak Waseem, Thomas Davidson, Dana Warmsley, and Ingmar Weber. 2017. Understanding Abuse: A Typology of Abusive Language Detection Subtasks. In Proceedings of the First Workshop on Abusive Language Online, pages 78–84, Vancouver, BC, Canada. Association for Computational Linguistics.
Cite (Informal):
Understanding Abuse: A Typology of Abusive Language Detection Subtasks (Waseem et al., ALW 2017)
Copy Citation:
PDF:
https://preview.aclanthology.org/emnlp-22-attachments/W17-3012.pdf