Constructive Language in News Comments

Varada Kolhatkar, Maite Taboada


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
Venues:
ALW | WS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
11–17
Language:
URL:
https://aclanthology.org/W17-3002
DOI:
10.18653/v1/W17-3002
Bibkey:
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, 2017)
Copy Citation:
PDF:
https://aclanthology.org/W17-3002.pdf