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
- Editors:
- Zeerak Waseem, Wendy Hui Kyong Chung, Dirk Hovy, Joel Tetreault
- 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
- 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)
- PDF:
- https://preview.aclanthology.org/dois-2013-emnlp/W17-3012.pdf