Automated collection of social network data to develop a factor model of network self-presentation

B. A. Nizomutdinov, A. S. Tropnikov, A. B. Uglova


On the basis of the conducted empirical research of information images of users, the leading components of network self-presentation were revealed: statistical, socio-demographic component, visual component and value-semantic component. The authors analyzed the hidden factors responsible for the formation of network self-presentation through the information image, studying the prognostic possibilities of social profile data analysis. Significant differences in the content of the information image and socio-psychological characteristics of users with different styles of network self-presentation were identified. Algorithms for collecting and processing open information from social network profiles, followed by factor analysis, as well as machine learning methods to determine the topics of communities and interesting pages to which users subscribe, are presented. The paper deals with ethical and legal issues of using data collection from user groups to create predictive models based on them, without notifying the users themselves. Also discusses theoretical issues of interdisciplinary design model information image, which becomes possible through multilateral analysis of the symbolic content of social network profile: linguistic and psychological evaluation of the semantic content of the content of socio-psychological overview of communication practices that are implemented in the network and parsing features of the network interface that specifies the structure of this profile.

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Sandra M., Oded N. Using Big Data as a window into consumers’ psychology // Current Opinion in Behavioral Sciences. 2017. № 18. P. 7 – 12.

Michal K. Mining Big Data to Extract Patterns and Predict Real-Life Outcomes // Psychological Methods. 2016. № 21. P. 493 – 506.

Barbakov O.M., Vinogradova M.V., Shatsky A. Social Portrait of Online Mass Media Audience in Russia // Media Watch. 2018. № 9. P. 383 – 396.

Xenos S., Ryan T. Who uses Facebook? An investigation into the relationship between the Big Five, shyness, narcissism, loneliness, and Facebook usage // Computers in Human Behavior. 2011. Vol. 27, № 5. P. 1658 – 1664.

Settanni M., Azucar D., Marengo D. Predicting the Big 5 personality traits from digital footprints on social media: A meta-analysis // Personality and Individual Differences. 2018. Vol 124. P. 150 – 159.

Sophie W.F., Susanne B.E., Patti V. Norms of online expressions of emotion: Comparing Facebook, Twitter, Instagram, and WhatsApp // SAGE. 2017. № 20. P. 1 – 19.

Kaiqi H., Qiao W., Zhenyang W. Natural color image enhancement and evaluation algorithm based on human visual system // Computer Vision and Image Understanding. 2006. Vol 1, № 103. P. 52 – 63.

Kim Y., Kim J.H. Using computer vision techniques on Instagram to link users’ personalities and genders to the features of their photos: An exploratory study // Information Processing & Management. 2018. Vol. 6, № 54. P. 1101 – 1114.

Gosling S., Gaddis S., Vazire S. Personality Impressions Based on Facebook Profiles // International Conference on Weblogs and Social Media. 2007.

The social network requires prohibiting the collection of data for banks // Kommerasnt [site].. URL:

The right to seek information on the Internet is at risk. The decision in the VKontakte case limits the work of search engines / Skolkovo [site]. URL: poisk-informacii-v-internete-pod-ugrozoy--reshenie-po-delu-vkontakte-ogranichivaet-rabotu- poiskovikov.aspx

Court ruling on intellectual property rights. / Intellectual Property Court [site]. URL: 53282523639c/A40-18827-2017_20180724_Reshenija_i_postanovlenija.pdf (дата обращения: 22.04.2019)

Kosinski M., Matz S., Gosling S. et al. Facebook as a social science research tool: Opportunities, challenges, ethical considerations and practical guidelines // American Psychologist. 2015. Vol. 70. N 6. P. 543—556.

Korneeva A., Zeremskaya Yu., Loyko O. Virtual space as a sphere of the personal identity’s formation // Journal of Economics and Social Sciences. 2016. № 8 (8). С. 31-35.

Wilson R.E., Gosling S.D., Graham L.T. A review of Facebook research in the social sciences // Perspectives on Psychological Science. 2012. 7. 3. 203-220.

Kosinski M., Stilwell D., Graepel T. Private traits and attributes are predictable from digital records of human behavior // Proc. the National Academy of Science of the United State of America. 2013. Vol. 110. P. 5802-5805. DOI: 10.1073/pnas.1218772110

Ross C., Orr E.S., Sisic M., Arseneault J.M., Simmering M.G., Orr R.R.: Personality and motivations associated with facebook use // Computers in Human Behavior. 2009. Vol. 25. P. 578-586. DOI: 10.1016/j.chb.2008.12.024

Krylova O.S., Vlasov D.A., Shishkov V.V., Alymov A.S., Ishin I.A., Kolesnikov I.Е. Petrov A.I. Opisanie informacionnogo obraza pol'zovatelya social'noj seti s uchetom ego psihologicheskoj harakteristiki // International Journal of Open Information Technologies. 2018. Vol. 4. С. 24-37.

Mairesse F., Walker M., Mehl M., Moore R. Using linguistic cues for the automatic recognition of personality in conversation and text // Journal of Artificial Intelligence Research. 2007. Vol. 30. P. 457-500. DOI: 10.1613/jair.2349


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