@InProceedings{pisarevskaya:2017:NLPmJ,
  author    = {Pisarevskaya, Dina},
  title     = {Deception Detection in News Reports in the Russian Language: Lexics and Discourse},
  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     = {74--79},
  abstract  = {News verification and automated fact checking tend to be very important issues
	in our world. The research is initial. We collected a corpus for Russian (174
	news reports, truthful and fake ones). We held two experiments, for both we
	applied SVMs algorithm (linear/rbf kernel) and Random Forest to classify the
	news reports into 2 classes: truthful/deceptive. In the first experiment, we
	used 18 markers on lexics level, mostly frequencies of POS tags in texts. In
	the second experiment, on discourse level we used frequencies of rhetorical
	relations types in texts. The classification task in the first experiment is
	solved better by SVMs (rbf kernel) (f-measure 0.65). The model based on RST
	features shows best results with Random Forest Classifier (f-measure 0.54) and
	should be modified. In the next research, the combination of different
	deception detection markers for the Russian language should be taken in order
	to make a better predictive model.},
  url       = {http://www.aclweb.org/anthology/W17-4213}
}