@InProceedings{safisamghabadi-EtAl:2017:ALW1,
  author    = {Safi Samghabadi, Niloofar  and  Maharjan, Suraj  and  Sprague, Alan  and  Diaz-Sprague, Raquel  and  Solorio, Thamar},
  title     = {Detecting Nastiness in Social Media},
  booktitle = {Proceedings of the First Workshop on Abusive Language Online},
  month     = {August},
  year      = {2017},
  address   = {Vancouver, BC, Canada},
  publisher = {Association for Computational Linguistics},
  pages     = {63--72},
  abstract  = {Although social media has made it easy for people to connect on a virtually
	unlimited basis, it has also opened doors to people who misuse it to undermine,
	harass, humiliate, threaten and bully others. There is a lack of adequate
	resources to detect and hinder its occurrence. In this paper, we  present our
	initial NLP approach to detect invective posts as a first step to eventually
	detect and deter cyberbullying. We crawl data containing profanities and then
	determine whether or not it contains invective. Annotations on this data are
	improved iteratively by in-lab annotations and crowdsourcing. We pursue
	different NLP approaches containing various typical and some newer techniques
	to distinguish the use of swear words in a neutral way from those instances in
	which they are used in an insulting way. We also show that this model not only
	works for our data set, but also can be successfully applied to different data
	sets.},
  url       = {http://www.aclweb.org/anthology/W17-3010}
}