@InProceedings{kolhatkar-taboada:2017:ALW1,
  author    = {Kolhatkar, Varada  and  Taboada, Maite},
  title     = {Constructive Language in News Comments},
  booktitle = {Proceedings of the First Workshop on Abusive Language Online},
  month     = {August},
  year      = {2017},
  address   = {Vancouver, BC, Canada},
  publisher = {Association for Computational Linguistics},
  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.},
  url       = {http://www.aclweb.org/anthology/W17-3002}
}