Abstract
Abusive language in online discourse negatively affects a large number of social media users. Many computational methods have been proposed to address this issue of online abuse. The existing work, however, tends to focus on detecting the more explicit forms of abuse leaving the subtler forms of abuse largely untouched. Our work addresses this gap by making three core contributions. First, inspired by the theory of impoliteness, we propose a novel task of detecting a subtler form of abuse, namely unpalatable questions. Second, we publish a context-aware dataset for the task using data from a diverse set of Reddit communities. Third, we implement a wide array of learning models and also investigate the benefits of incorporating conversational context into computational models. Our results show that modeling subtle abuse is feasible but difficult due to the language involved being highly nuanced and context-sensitive. We hope that future research in the field will address such subtle forms of abuse since their harm currently passes unnoticed through existing detection systems.- Anthology ID:
- 2021.eacl-main.179
- Volume:
- Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume
- Month:
- April
- Year:
- 2021
- Address:
- Online
- Editors:
- Paola Merlo, Jorg Tiedemann, Reut Tsarfaty
- Venue:
- EACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 2083–2099
- Language:
- URL:
- https://aclanthology.org/2021.eacl-main.179
- DOI:
- 10.18653/v1/2021.eacl-main.179
- Cite (ACL):
- Sunyam Bagga, Andrew Piper, and Derek Ruths. 2021. “Are you kidding me?”: Detecting Unpalatable Questions on Reddit. In Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pages 2083–2099, Online. Association for Computational Linguistics.
- Cite (Informal):
- “Are you kidding me?”: Detecting Unpalatable Questions on Reddit (Bagga et al., EACL 2021)
- PDF:
- https://preview.aclanthology.org/landing_page/2021.eacl-main.179.pdf