The presented project is intended to make use of growing amounts or textual data in social networks in the Russian language, In order to Hnd Ungulstlc correlates of the Dark Triad personality traits, comprising non-clinical Nareissism, Machiavellianism and Psychopathy. The baekgronnd for the ilwestigation includes, on the one haotl, psychological research on these phenomena and their measurement instruments, and on the other haod, recent advaoces In computational stylometry and text-based author profiling. The measures for these psychological phenomena are provided by recognized self-report psychological surveys adapted to Russian. Morphological and semantic analysis are applied to investigate the relationship between the Dark traits and their linguistic manifestation in social network texts. Slgnlflcant morphological and semantic correlates of Narcissism, MachlavelUanlsm and Psychopathy are ldentllled and compared to respective advaoces In Engltsh author proftUng. In order to deepen our underslanding of the relation between these psychological characteristics aod natural language use, the identified linguistic features are Interpreted In terms of the line-grained factor structure of the Dark traits. Identifying correlated features is a step towards automatic Dark trait prediction aod early detection of the potentially harmful mental states.

Original languageEnglish
Title of host publicationProceedings of the AINL FRUCT 2016 Conference
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages72 - 79
ISBN (Electronic)9789526839783
StatePublished - 3 Apr 2017
EventArtificial Intelligence and Natural Language FRUCT Conference - Saint-Petersburg, Russian Federation
Duration: 10 Nov 201612 Nov 2016
Conference number: 5
http://ainlconf.ru/2016

Conference

ConferenceArtificial Intelligence and Natural Language FRUCT Conference
Abbreviated titleAINL FRUCT 2016
Country/TerritoryRussian Federation
CitySaint-Petersburg
Period10/11/1612/11/16
Internet address

    Scopus subject areas

  • Artificial Intelligence

ID: 7609549