ISSN 0868-4871
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ISSN 0868-4871
Two Scenarios for the Future of Youth: The Results of Mapping Groups in the Social Network Vkontakte in the City of Tomsk

Two Scenarios for the Future of Youth: The Results of Mapping Groups in the Social Network Vkontakte in the City of Tomsk

Abstract

Social networks are an environment that refl ects the social practices of youth. From the situational activity of each network group, large clusters are formed which accumulate attitudes of the corresponding layer of young people. This study focuses on regional groups of the social network VKontakte in the city of Tomsk. Data collection and visualization create a methodology for mapping this information space (“community detection”) that is relevant in network analysis. The presence of two socially diff erentiating strategies of behavior among the youth of Tomsk were substantiated by the study. They feature diff erent everyday practices, values, reference groups and information sources, and self-stigmatization systems. “Low” sociocultural practices correspond to a social group that can be described as a “group without a future,” with a negative identity. The practices of another social group, aimed at obtaining higher education and building a career, diff er radically from those of the former. Its representatives are guided by foreign sources, organizations and models. Signifi cant attention in the study is given over to issues of methodology, the mapping process and the interpretation of research results.

References

  1. Azaouzi, M., Rhouma, D., and Romdhane, L. B. “Community Detection in Large-scale Social Networks: State-of-the-art and Future Directions,” Social Network Analysis and Mining, Vol. 9, No. 1, 2019, pp. 1–32. 
  2. Coleman, J. The Foundations of Social Theory. Cambridge: Belknap of Harvard University Press, 1990. 
  3. Gradosel’skaia, G. V. Setevye izmereniia v sotsiologii: Uchebnoe posobie. Moscow: Izdatel’skii dom «Novyi uchebnik», 2004. 
  4. Gradosel’skaia, G. V., Shcheglova, T. E., and Karpov, I. A. “Kartirovanie politicheski aktivnykh grupp v Feisbuke: dinamika 2013–2018 gg.,” Voprosy kiberbezopasnosti, No. 4, 2019, pp. 94–104. 
  5. Gradoselskaya, G., Karpov, I., and Shcheglova, T. “Mapping of Politically Active Groups on Social Networks of Russian Regions (On the Example of Karachay-Cherkessia Republic),” Network Algorithms, Data Mining, and Applications (NET, Moscow, Russia, May 2018), eds. I. Bychkov, V. Kalyagin, P. Pardalos, and O. Prokopyev. Cham: Springer, 2020, pp. 187–200. 
  6. Harary, F. Teoriia grafov. Moscow: Mir, 1973. 
  7. Moosavi, S. A., Jalali, M., Misaghian, N., Shamshirband, Sh., and Anisi, M. H. “Community Detection in Social Networks Using User Frequent Pattern Mining,” Knowledge and Information Systems, Vol. 51, No. 1, 2017, pp. 159–186. 
  8. Ore, Ø. Teoriia grafov. Moscow: Nauka, 1980. 
  9. Sarswat, A., Jami, V., and Guddet, R. M. R. “A Novel Two-step Approach for Overlapping Community Detection in Social Networks,” Social Network Analysis and Mining, Vol. 7, No. 1, 2017, Article 47. 
  10. Wang, D., Li, J., Xu, K., and Wu, Y. “Sentiment Community Detection: Exploring Sentiments and Relationships in Social Networks,” Electronic Commerce Research, Vol. 17, No. 1, 2017, pp. 103–132. 
  11. Wasserman, S., and Faust, K. Social Network Analysis Methods and Applications. Cambridge: Cambridge University Press, 1994.
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Keywords: network analysis, community detection, youth political activity, Tomsk

Available in the on-line version with: 31.12.2020

To cite this article
Number 4, 2020