Journal sections
Archive and statistics
Log in

Печатный вестник PRINTED
Издательство МГОУ Publishing house the

Our address: 105005, Moscow, Radio street,10a, office 98.

+7 (495) 780-09-42 add. 1740,
+7 (495) 723-56-31


Work schedule: Monday to Thursday from 10-00 to 17-30,

Friday from 10:00 to 16-00,

lunch break from 13:00 to 14-00.



BK Facebook Telegram Twitter Instagram

Bulletin of the MRSU / Section "Psychology" / 2020 № 3.


A.N. Voronin, T.A. Kubrak, I.V. Smirnov, M.A. Stankevich



UDC Index: 159.9.07; 004.8

Date of publication: 28.09.2020

The full text of the article

Downloads count Downloads count: 28


Aim. Presentation of regression models of the subjectivity of network communities based on automatically determined indicators of the content relational situational analysis (RSA).
Methodology. To develop these models 64 network communities of various thematic focus from the open segment of social networks (Facebook, VKontakte, Odnoklassniki, Pikabu, Telegramm, etc.) were analyzed. The networks communities texts were subjected to psycholinguistic analysis using a previously developed list of discourse markers, and the results allowed to identify indicators of subjectivity. Automatic relational situational analysis of texts was performed using an RSA machine developed at the Institute for System Analysis of the Russian Academy of Sciences.
Results. Comprehensive regression models of satisfactory quality were constructed for all indicators of subjectivity.
Research implications. The use of the obtained regression models will allow to monitor various sectors of the Runet in an automated mode and to assess he subjectivity of the content.

Key words

internet, network community subjectivity, discourse markers, text mining, digital trails, relational-situational analysis of text, regression models

List of references

1. Voronin A. N. [Methodological problems of studying the subjectivity of network communities]. In: Psihologiya i Psihotekhnika [Psychology and Psychotechnics], 2019, no. 3. Available at: (accessed: 09.04.2020).
2. Voronin A. N., Kovaleva Yu. V. [Changing the subjectivity of the network community in the process of trolling]. In: Social’naya i ekonomicheskaya psihologiya [Social and Economic Psychology], 2019, vol. 4, no. 3 (15), pp. 25–61.
3. Vuchkov I., Boyadzhieva L., Solakov E. Prikladnoj linejnyj regressionnyj analiz [Applied Linear Regression Analysis; transl. from Bulg. by Yu. P. Adler]. Moscow, Finance and statistics, 1987. 239 p.
4. Emel’yanova T. P., Zhuravlev A. L. [Psychology of large social groups as collective subjects]. In: Psihologicheskij zhurnal [Psychological Magazine], 2009, vol. 30, no. 3, pp. 5–15.
5. Zhuravlev A. L. [Psychology of collective subject]. In: Psihologiya individual’nogo i gruppovogo sub’ekta [Psychology of the individual and group subject; K. A. Abul’hanova, V. A. Barabanshchikov, A. V. Brushlinskij and etc.]. Moscow, Per Se, 2002, pp. 51–81.
6. Zhuravlev A. L. [The collective subject as a phenomenon and concept in modern psychology]. In: Zhuravlev A. L., Sergienko E. A., eds. Razrabotka ponyatii sovremennoi psikhologii [Development of concepts of modern psychology]. Moscow, Institute of Psychology RAS Publ., 2018, pp. 116–161.
7. Kovalyov A. K, Kuznecova Yu. M., Minin A. N., Penkina M. Yu., Smirnov I. V., Stankevich M. A., Chudova N. V. Methods for identifying psychological characteristics of the author in the text (on example of aggressiveness). In: Voprosy kiberbezopasnosti [Cybersecurity issues], 2019, no. 4 (32), pp. 72–79.
8. Osipov G. S., Smirnov I. V., Tihomirov I. A. [Relational-situational method of searching and analyzing texts and its applications]. In: Iskusstvennyj intellekt i prinyatie reshenij [Artificial intelligence and decision making], 2008, no. 2, pp. 3–10.
9. Voronin A. N., Pavlova N. D., Grebenshchikova T. A., Kubrak T. A., Smirnov I. V. [Assessment of subjectivity of network communities: comparison of discourse markers and PCA indicators]. In: Social’naya i ekonomicheskaya psihologiya [Social and Economic Psychology], 2020, vol. 5, no. 2 (18), pp. 330–364.
10. Pavlova N. D. [The Interactive Aspect of Discourse: Approaches to Research]. In: Psihologicheskij zhurnal [Psychological journal], 2005, vol. 26, no 4, pp. 66–76.
11. Pavlova N. D., Kubrak T. A., Grebenshchikova T. A. [Study of the dynamics of subjectivity of network communities by its manifestation in discourse]. In: Psihologicheskie issledovaniya [Psychological research], 2020, vol. 13, no 70. Available at: (accessed: 09.04.2020).
12. Pogorskij E. K. [Features of the digital humanities]. In: Znanie. Ponimanie. Umenie [Knowledge. Understanding. Skill], 2014, no. 5. Available at: (accessed: 09.04.2020).
13. Smirnov I. V., Shelmanov A. O., Kuznecova E. S., Hramoin I. V. [Semantic-syntactic analysis of natural languages. Part II. The method of semantic-syntactic analysis of texts]. In: Iskusstvennyj intellekt i prinyatie reshenij [Artificial intelligence and decision making], 2014, no. 1, pp. 11–24.
14. Enikolopov S. N., Kuznecova Yu. M., Smirnov I. V., Stankevich M. A., Chudova N. V. [Creation of a tool for automatic text analysis in the interests of socio-humanitarian research. Part 1. Methodological and methodological aspects]. In: Iskusstvennyj intellekt i prinyatie reshenij [Artificial intelligence and decision making], 2019, no. 2, pp. 28–38.
15. Voronin A. N., Grebenschikova T. A., Kubrak T.  A., Pavlova N.  D. The subjectness of the network community: a comparison of psychometric models of the discursive markers manifestation in content. In: Vestnik Moskovskogo gosudarstvennogo oblastnogo universiteta. Seriya: Psihologicheskie nauki [Bulletin of the Moscow Region State University. Series: Psychology], 2019, no.  3, рр. 6–24.
16. Kuznecova Yu. M., Kuruzov I. A., Smirnov I. V., Stankevich M. A., Starostina E. V., Chudova N. V. [Textual manifestations of frustration of a user of social networks]. In: Medialingvistika [Media linguistics], 2020. № 7 (1), pp. 4–15.
17. Al-Mosaiwi M., Johnstone T. In an Absolute State: Elevated Use of Absolutist Words Is a Marker Specific to Anxiety, Depression, and Suicidal Ideation. In: Clinical Psychological Science, 2018, no. 6 (4), pp. 529–542.
18. Azucar D., Marengo D., Settanni M. Predicting the big 5 personality traits from digital footprints on social media: A meta-analysis. In: Personality and Individual Differences, 2018, vol. 124, pp. 150–159.
19. Drouin M., Boyd R. L., Greidanus Romaneli M. Predicting recidivism among internet child sex sting offenders using psychological language analysis. In: Cyberpsychology, Behavior, and Social Networking, 2018, vol. 21, pp. 78–83.
20. Grunebaum M. F. Suicidology meets “Big Data”. In: Journal of Clinical Psychiatry, 2015, vol. 76, no. 3, pp. e383–e384.
21. Dehghani M., Johnson K. M., Garten J., Boghrati R., Hoover J., Balasubramanian V., Singh A., Shankar Y., Pulickal L., Rajkumar A., Parmar N. J. TACIT: An open-source text analysis, crawling, and interpretation tool. In: Behavior Research Methods, 2017, no. 49 (2), pp. 538–547.
22. Jashinsky J., Burton S. H., Hanson C. L., West J., Giraud-Carrier C. Tracking suicide risk factors through Twitter in the US. In: Crisis. 2014, vol. 35, pp. 51–59.
23. Lambiotte R., Kosinski M. Tracking the Digital Footprints of Personality. In: Proceedings of the Institute of Electrical and Electronics Engineers (IEEE), 2014, no. 102 (12), pp. 1934–1939.
24. Manovich L. Cultural Data: Possibilities and Limitations of Digitized Archives. In: Museum and Archive on the Move. Changing Cultural Institutions in the Digital Era. Berlin, Boston: De Gruyter, 2017. P. 259–276.
25. Pennebaker J. W. Campbell R. S. The effects of writing about traumatic experience. In: Clinical Quarterly, 2000, no. 9, pp. 17–21.
26. Potter J. Discoursive psychology and the study of Naturally occurring Talk. In: Qualitative Research. SAGE Publications, 2011, pp. 187–207.
27. Soffer O. The Internet and National Solidarity: A Theoretical Analysis. In: Communication Theory, 2013, vol. 23, no. 1, pp. 48–66.
28. Voronin A. N., Grebenschikova T. A., Kubrak T. A., Nestik T. A., Pavlova N. D. The Study of Network Community Capacity to be a Subject: Digital Discursive Footprints. In: Behavioral Sciences, 2019, no. 9. Available at: (accessed: 09.04.2020).
29. Schwartz A. H., Eichstaedt J., Kern M., Park G., Sap M., Stillwell D., Ungar L. Towards assessing changes in degree of depression through Facebook. In: Proceedings of the Workshop on Computational Linguistics and Clinical Psychology. Association for Computational Linguistics, 2014, pp. 118–125.
30. Mairesse F., Walker M. A., Mehl M. R., Moore R. K. Using Linguistic Cues for the Automatic Recognition of Personality in Conversation and Text. In: Journal of Artificial Intelligence Research, 2007, no. 30, pp. 457–500.
31. Wilson S. R. Natural language processing for personal values and human activities: a dis. … Doctor of Philosophy. Michigan, 2019. 146 p. University of Michigan. 2019. Available at: (accessed: 09.04.2020).
32. Yarkoni T. Personality in 100,000 words: A large-scale analysis of personality and word use among bloggers. In: Journal of Research in Personality, 2010, no. 44, pp. 363–373.

Лицензия Creative Commons