@inproceedings{pisarevskaya-etal-2017-deception,
    title = "Deception Detection for the {R}ussian Language: Lexical and Syntactic Parameters",
    author = "Pisarevskaya, Dina  and
      Litvinova, Tatiana  and
      Litvinova, Olga",
    editor = "Makary, Mireille  and
      Oakes, Michael",
    booktitle = "Proceedings of the 1st Workshop on Natural Language Processing and Information Retrieval associated with {RANLP} 2017",
    month = sep,
    year = "2017",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Inc.",
    url = "https://doi.org/10.26615/978-954-452-038-0_001",
    doi = "10.26615/978-954-452-038-0_001",
    pages = "1--10",
    abstract = "The field of automated deception detection in written texts is methodologically challenging. Different linguistic levels (lexics, syntax and semantics) are basically used for different types of English texts to reveal if they are truthful or deceptive. Such parameters as POS tags and POS tags n-grams, punctuation marks, sentiment polarity of words, psycholinguistic features, fragments of syntaсtic structures are taken into consideration. The importance of different types of parameters was not compared for the Russian language before and should be investigated before moving to complex models and higher levels of linguistic processing. On the example of the Russian Deception Bank Corpus we estimate the impact of three groups of features (POS features including bigrams, sentiment and psycholinguistic features, syntax and readability features) on the successful deception detection and find out that POS features can be used for binary text classification, but the results should be double-checked and, if possible, improved.",
}