Abstract
We discuss the characteristics of constructive news comments, and present methods to identify them. First, we define the notion of constructiveness. Second, we annotate a corpus for constructiveness. Third, we explore whether available argumentation corpora can be useful to identify constructiveness in news comments. Our model trained on argumentation corpora achieves a top accuracy of 72.59% (baseline=49.44%) on our crowd-annotated test data. Finally, we examine the relation between constructiveness and toxicity. In our crowd-annotated data, 21.42% of the non-constructive comments and 17.89% of the constructive comments are toxic, suggesting that non-constructive comments are not much more toxic than constructive comments.- Anthology ID:
- W17-3002
- 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:
- 11–17
- Language:
- URL:
- https://aclanthology.org/W17-3002
- DOI:
- 10.18653/v1/W17-3002
- Cite (ACL):
- Varada Kolhatkar and Maite Taboada. 2017. Constructive Language in News Comments. In Proceedings of the First Workshop on Abusive Language Online, pages 11–17, Vancouver, BC, Canada. Association for Computational Linguistics.
- Cite (Informal):
- Constructive Language in News Comments (Kolhatkar & Taboada, ALW 2017)
- PDF:
- https://preview.aclanthology.org/auto-file-uploads/W17-3002.pdf