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).- Anthology ID:
- E17-4005
- Volume:
- 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
- Editors:
- Florian Kunneman, Uxoa Iñurrieta, John J. Camilleri, Mariona Coll Ardanuy
- Venue:
- EACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 43–52
- Language:
- URL:
- https://aclanthology.org/E17-4005
- DOI:
- Cite (ACL):
- Olga Litvinova, Pavel Seredin, Tatiana Litvinova, and John Lyell. 2017. Deception detection in Russian texts. In Proceedings of the Student Research Workshop at the 15th Conference of the European Chapter of the Association for Computational Linguistics, pages 43–52, Valencia, Spain. Association for Computational Linguistics.
- Cite (Informal):
- Deception detection in Russian texts (Litvinova et al., EACL 2017)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-3/E17-4005.pdf