@InProceedings{potash-EtAl:2017:NLPmJ,
  author    = {Potash, Peter  and  Romanov, Alexey  and  Gronas, Mikhail  and  Rumshisky, Anna  and  Gronas, Mikhail},
  title     = {Tracking Bias in News Sources Using Social Media: the Russia-Ukraine Maidan Crisis of 2013--2014},
  booktitle = {Proceedings of the 2017 EMNLP Workshop: Natural Language Processing meets Journalism},
  month     = {September},
  year      = {2017},
  address   = {Copenhagen, Denmark},
  publisher = {Association for Computational Linguistics},
  pages     = {13--18},
  abstract  = {This paper addresses the task of identifying the bias in news articles
	published during a political or social conflict. We create a silver-standard
	corpus based on the actions of users in social media. Specifically, we
	reconceptualize bias in terms of how likely a given article is to be shared or
	liked by each of the opposing sides. We apply our methodology to a dataset of
	links collected in relation to the Russia-Ukraine Maidan crisis from 2013-2014.
	 We show that on the task of predicting which side is likely to prefer a given
	article, a Naive Bayes classifier can record 90.3% accuracy looking only at
	domain names of the news sources. The best accuracy of 93.5% is achieved by a
	feed forward neural network. We also apply our methodology to gold-labeled set
	of articles annotated for bias, where the aforementioned Naive Bayes classifier
	records 82.6% accuracy and a feed-forward neural networks records 85.6%
	accuracy.},
  url       = {http://www.aclweb.org/anthology/W17-4203}
}