Abstract
We address the task of automatically detecting toxic content in user generated texts. We fo cus on exploring the potential for preemptive moderation, i.e., predicting whether a particular conversation thread will, in the future, incite a toxic comment. Moreover, we perform preliminary investigation of whether a model that jointly considers all comments in a conversation thread outperforms a model that considers only individual comments. Using an existing dataset of conversations among Wikipedia contributors as a starting point, we compile a new large-scale dataset for this task consisting of labeled comments and comments from their conversation threads.- Anthology ID:
- W19-3514
- Volume:
- Proceedings of the Third Workshop on Abusive Language Online
- Month:
- August
- Year:
- 2019
- Address:
- Florence, Italy
- Editors:
- Sarah T. Roberts, Joel Tetreault, Vinodkumar Prabhakaran, Zeerak Waseem
- Venue:
- ALW
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 129–134
- Language:
- URL:
- https://aclanthology.org/W19-3514
- DOI:
- 10.18653/v1/W19-3514
- Cite (ACL):
- Mladen Karan and Jan Šnajder. 2019. Preemptive Toxic Language Detection in Wikipedia Comments Using Thread-Level Context. In Proceedings of the Third Workshop on Abusive Language Online, pages 129–134, Florence, Italy. Association for Computational Linguistics.
- Cite (Informal):
- Preemptive Toxic Language Detection in Wikipedia Comments Using Thread-Level Context (Karan & Šnajder, ALW 2019)
- PDF:
- https://preview.aclanthology.org/teach-a-man-to-fish/W19-3514.pdf