Deception detection in Russian texts

Psychology studies show that people detect deception no more accurately than by chance, and it is therefore important to develop tools to enable the detection of deception. The problem of deception detection has been studied for a significant amount of time, however in the last 1015 years we have seen methods of computational linguistics being employed with greater frequency. Texts are processed using different NLP tools and then classified as deceptive/truthful using modern machine learning methods. While most of this research has been performed for the English language, Slavic languages have never been the focus of detection deception studies. This paper deals with deception detection in Russian narratives related to the theme ”How I Spent Yesterday”. It employs a specially designed corpus of truthful and deceptive texts on the same topic from each respondent, such that 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 average amount of parameters, a majority of which were related to Part-of-Speech, lexical-semantic group, and other frequencies. Using standard statistical analysis, statistically significant differences between false and truthful Russian texts was uncovered. On the basis of the chosen parameters our classifier reached an accuracy of 68.3%. The accuracy of the model was found to depend on the author’s gender.