@inproceedings{kolhatkar-taboada-2017-constructive,
title = "Constructive Language in News Comments",
author = "Kolhatkar, Varada and
Taboada, Maite",
editor = "Waseem, Zeerak and
Chung, Wendy Hui Kyong and
Hovy, Dirk and
Tetreault, Joel",
booktitle = "Proceedings of the First Workshop on Abusive Language Online",
month = aug,
year = "2017",
address = "Vancouver, BC, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W17-3002/",
doi = "10.18653/v1/W17-3002",
pages = "11--17",
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."
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%0 Conference Proceedings
%T Constructive Language in News Comments
%A Kolhatkar, Varada
%A Taboada, Maite
%Y Waseem, Zeerak
%Y Chung, Wendy Hui Kyong
%Y Hovy, Dirk
%Y Tetreault, Joel
%S Proceedings of the First Workshop on Abusive Language Online
%D 2017
%8 August
%I Association for Computational Linguistics
%C Vancouver, BC, Canada
%F kolhatkar-taboada-2017-constructive
%X 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.
%R 10.18653/v1/W17-3002
%U https://aclanthology.org/W17-3002/
%U https://doi.org/10.18653/v1/W17-3002
%P 11-17
Markdown (Informal)
[Constructive Language in News Comments](https://aclanthology.org/W17-3002/) (Kolhatkar & Taboada, ALW 2017)
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.