@InProceedings{litvinova-EtAl:2017:EACLSRW17,
  author    = {Litvinova, Olga  and  Seredin, Pavel  and  Litvinova, Tatiana  and  Lyell, John},
  title     = {Deception detection in Russian texts},
  booktitle = {Proceedings of the Student Research Workshop at the 15th Conference of the European Chapter of the Association for Computational Linguistics},
  month     = {April},
  year      = {2017},
  address   = {Valencia, Spain},
  publisher = {Association for Computational Linguistics},
  pages     = {43--52},
  abstract  = {Humans are known to detect deception in speech randomly and it is therefore
	important to develop tools to enable them to detect deception. The problem of
	deception detection has been studied for a significant amount of time, however
	the last 10-15 years have seen methods of computational linguistics being
	employed. Texts are processed using different NLP tools and then classified as
	deceptive/truthful using machine learning methods. While most research has been
	performed for English, Slavic languages have never been a focus of detection
	deception studies. The paper deals with deception detection in Russian
	narratives. It employs a specially designed corpus of truthful and deceptive
	texts on the same topic from each respondent, N = 113. The texts were processed
	using Linguistic Inquiry and Word Count software that is used in most studies
	of text-based deception detection. The list of parameters computed using the
	software was expanded due to the designed users' dictionaries. A variety of
	text classification methods was employed. The accuracy of the model was found
	to depend on the author's gender and text type (deceptive/truthful).},
  url       = {http://www.aclweb.org/anthology/E17-4005}
}