@InProceedings{kennedy-EtAl:2017:ALW1,
  author    = {Kennedy, George  and  McCollough, Andrew  and  Dixon, Edward  and  Bastidas, Alexei  and  Ryan, John  and  Loo, Chris  and  Sahay, Saurav},
  title     = {Technology Solutions to Combat Online Harassment},
  booktitle = {Proceedings of the First Workshop on Abusive Language Online},
  month     = {August},
  year      = {2017},
  address   = {Vancouver, BC, Canada},
  publisher = {Association for Computational Linguistics},
  pages     = {73--77},
  abstract  = {This work is part of a new initiative to use machine learning to identify
	online harassment in social media and comment streams. Online harassment goes
	under-reported due to the reliance on humans to identify and report harassment,
	reporting that is further slowed by requirements to fill out forms providing
	context. In addition, the time for moderators to respond and apply human
	judgment can take days, but response times in terms of minutes are needed in
	the online context. Though some of the major social media companies have been
	doing proprietary work in automating the detection of harassment, there are few
	tools available for use by the public. In addition, the amount of labeled
	online harassment data and availability of cross-platform online harassment
	datasets is limited. We present the methodology used to create a harassment
	dataset and classifier and the dataset used to help the system learn what
	harassment looks like.},
  url       = {http://www.aclweb.org/anthology/W17-3011}
}