Cognitive Complexity and Communicative Context: Reflection of User Intelligence in Social Media Texts

Occupation: research fellow of the Institute of Psychology of the Russian Academy of Sciences, senior research fellow of Moscow State University of Psychology and Pedagogy
Affiliation:
Russian Academy of Sciences Institute of Psychology
Moscow State University of Psychology and Pedagogy
Address: Russian Federation, Moscow

A. A. Grigoriev

Occupation: Leading Researcher
Affiliation: Russian Academy of Sciences Institute of Psychology
Address: Russian Federation, Moscow

E.M. Lapteva

Occupation: independent researcher
Affiliation:
Address: Russian Federation, Moscow

A.S. Panfilova

Occupation: researcher
Affiliation: Russian Academy of Sciences Institute of Psychology
Address: Russian Federation, Moscow

DMITRY USHAKOV

Occupation: Director of RAS Institute of Psychology
Affiliation: Russian Academy of Sciences Institute of Psychology
Address: Russian Federation, Moscow

Journal name

Psikhologicheskii zhurnal

Edition

Volume 44 Issue 1

Pages

70-80

Abstract

The focus of this article is the reflection of intelligence as ability in social media texts. The authors have developed a model of communicative environments according to which the manifestation of intelligence in a message depends on the communicative situation in which the information is transmitted. Thus, the cognitive complexity of texts is a consequence not only of the author's intellectual capacity, but also of his willingness and ability to adapt the complexity of messages to the characteristics of the recipient. This paper analyses data from individual social media profiles in relation to user intelligence test scores, as well as similar data obtained at a regional level. The study involved 438 subjects who took an intelligence test and provided access to their social media profiles. Facebook users were found to be, on average, more intelligent than VKontakte users, posting more posts containing text, posting less often overall. No significant differences in post characteristics were found between the two social networks. However, differences in the nature of the relationship between intelligence and cognitive complexity of messages were demonstrated for different social networks and for male and female subsamples of users. Correlations were found to be higher at the regional level compared to the individual level, higher for Facebook compared to VKontakte and higher for men compared to women. It is concluded that indicators of texts’ cognitive complexity from social media do reflect the intelligence of their authors, but the extent of this reflection depends on the characteristics of the communicative situation

Keywords

intelligence, social networks, communicative context, cognitive complexity of texts, gender differences

Acknowledgment

The research was supported by RFBR grant no. 18-29-22095.

Publication date

26.02.2023

Number of characters

33487

Cite

GOST
MLA	Valueva, Е. A., Grigoriev, A. A, Lapteva, E.M, Panfilova, A.S, USHAKOV, DMITRY "Cognitive Complexity and Communicative Context: Reflection of User Intelligence in Social Media Texts." Psikhologicheskii zhurnal 44.1 (2023):70-80. DOI: 10.31857/S020595920024352-3
APA	Valueva Е., Grigoriev A., Lapteva E., Panfilova A., USHAKOV D. (2023). Cognitive Complexity and Communicative Context: Reflection of User Intelligence in Social Media Texts. Psikhologicheskii zhurnal 44(1), pp.70-80 DOI: 10.31857/S020595920024352-3

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1. Abul'hanova K.A. Rossijskaja problema svobody, odinochestva i smirenija. Psikhologicheskii zhurnal. 1999. V. 20. № 5. P. 5–14.

2. Baturin H.A., Kurganskij H.A. Universal'nyj intellektual'nyj test (UIT SPCh-M): Rukovodstvo. Cheljabinsk: Psihologicheskij centr “PsiHRON”, 2003.

3. Brushlinskij A.V. Myshlenie i prognozirovanie. Moscow: Mysl', 1979.

4. Brushlinskij A.V. Problemy psihologii sub#ekta. Moscow: Nauka, 1994.

5. Valueva E.A., Danilevskaja N.M., Lapteva E.M., Ushakov D.V. Kognitivnaja slozhnost' hudozhestvennyh tekstov dlja detej: kvantitativnye metody ocenki. Voprosy psiholingvistiki. 2017. V. 31. № 1. P. 42–61.

6. Valueva E.A., Lapteva E.M., Grigor'ev A.A. Intellekt regionov skvoz' prizmu social'nyh setej. Psihologija. Zhurnal Vysshej shkoly jekonomiki. 2021. V. 18. № 1. P. 129–144.

7. Druzhinin V.N. Kognitivnye sposobnosti: struktura, diagnostika, razvitie. Moscow: Per Sje, Saint-Petersburg: Imaton-M, 2001. 224 p.

8. Zavalishina D.N. Psihologicheskij analiz operativnogo myshlenija. Moscow: Nauka, 1985. 221 p.

9. Ljusin D.V., Sysoeva T.A. Jemocional'naja okraska imen sushhestvitel'nyh: baza dannyh ENRuN. Psikhologicheskii zhurnal. 2017. V. 38. № 2. P. 122–131.

10. Piazhe Zh. Izbrannye trudy. Moscow: Prosveshhenie, 1969.

11. Ponomarev Ja.A. Psihologija tvorchestva. Moscow: Nauka, 1976.

12. Rubinshtejn S.L. Bytie i soznanie. Chelovek i mir. Saint-Petersburg: Piter, 2003. 512 p.

13. Shadrikov V.D. Dejatel'nost' i sposobnosti. Moscow: Izd. Korporacija “Logos”, 1994. 320 p.

14. Bors D.A., Stokes T.L. Raven’s Advanced Progressive Matrices: Norms for First-Year University Students and the Development of a Short Form. Educational and Psychological Measurement. 1998. V. 58. № 3. P. 382–398.

15. Grigoriev A., Ushakov D., Valueva E.A., Zirenko M., Lynn R. Differences in educational attainment, socio-economic variables and geographical location across 79 provinces of the Russian Federation. Intelligence. 2016. V. 58. P. 14–17.

16. Kosinski M., Stillwell D., Graepel T. Private traits and attributes are predictable from digital records of human behavior. Proceedings of the National Academy of Sciences. National Academy of Sciences, 2013. V. 110. № 15. P. 5802–5805.

17. Lynn R., Vanhanen T. National IQs: A review of their educational, cognitive, economic, political, demographic, sociological, epidemiological, geographic and climatic correlates. Intelligence. Elsevier Inc., 2012. V. 40. № 2. P. 226–234.

18. Smirnov I. Estimating educational outcomes from students’ short texts on social media. EPJ Data Science 2020 9:1. SpringerOpen, 2020. V. 9. № 1. P. 1–11.

19. Smirnov I. The digital flynn effect: Complexity of posts on social media increases over time. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 2017. V. 10540 LNCS. P. 24–30.