Research ArticleAnatoly L. Zhuravlev, Академик РАН, Doctor of Psychology, Professor, Institute of Psychology RAS, Moscow, Russia alzhuravlev2018@yandex.ruORCID ID=0000-0002-2555-7599Dzhuletta A. Kitova Doctor of Psychology, Professor, Institute of Psychology RAS, Moscow, Russia j-kitova@yandex.ruORCID ID=0000-0002-8185-3974Education as a Resource for Socio-Economic Development of Russia: Perceptions of Social Network Users. Vestnik instituta sotziologii. 2023. Vol. 14. No. 4. P. 251-270The study was financially supported by RNF, within the framework of scientific project No. 21-18-00541.Дата поступления статьи: 04.09.2023Topic: Educational system: resources and development potentialFor citation: Zhuravlev A. L., Kitova D. A. Education as a Resource for Socio-Economic Development of Russia: Perceptions of Social Network Users. Vestnik instituta sotziologii. 2023. Vol. 14. No. 4. P. 251-270DOI: https://doi.org/10.19181/vis.2023.14.4.14. EDN: SOTZAQТекст статьиAbstractIn addition to the traditional reflections on the explicit functions of education (reproduction of the socio-professional structure of society) and its latent forms (reproduction of distribution relations), the tasks of state development are added, which become highly relevant from the point of view of economic achievements and from the point of view of meeting the needs of man and society, that determine the relevance of the researched problem. The purpose of the study was to identify the population’s trivial ideas about the functional capabilities of education that can have an impact on the development of the state as a whole. The methodological approach is based on the fact that the perception of macroeconomic processes determined by the development of education will be reflected in the trivial ideas of the population, that can be studied through structural and content analysis of the texts of messages on social networks. The study used methods of grounded theory, neural network analysis, analysis of the frequency of occurrence and emotional background of words, content and expert analysis. As a result of the study, the following main conclusions were formulated. According to users of social networks, the leading functions of education are related to its ability to influence the social, economic and political development of society and the state, including as a geopolitical subject. Users associate education with various levels of development of socio-economic processes - from personal to national. The functional essence of education as a resource for the macroeconomic development of the state dominates over other aspects (micro- and meso-levels) of ideas and demonstrates a clearly defined hierarchical relationship of priorities, arranged in the following order: national, historical, systemic, global, institutional, regional and meso-economic. As part of the discussion of educational processes, differences invariably arise between supporters of state-oriented and socially-centered ways of developing society. The historical conditionality of Russian education among users correlates with the peculiarities of its development in the USSR - there is no connection to earlier periods of history. Education from a functional perspective is assessed by users as a benefit for individuals and society, the achievement of which requires significant time and socio-economic costs on the part of all its participants (the state, teachers and students). Insufficient satisfaction with higher education is evident among the majority of users and is due to an assessment of its current state. The identified results require further specification, for example, the study of algorithms proposed to overcome the current situation.Keywordsmacro-psychology, functions of education, trivial perceptions, frequency analysis of words, neural network model, emotional background, social contextsReferences Aprelikova N. R., Kitova D. A. Strukture of student youth needs in knowledge of psychology. Institut psikhologii RAN. Sotsial′naya i ekonomicheskaya psikhologiya, 2018: 4: 2(14): 110–133 (in Russ.). EDN: XSMSPZ. Gorshkov M. K. Oneconomic factors of economic growth: untapped reserves. Gumanitarnyye nauki. Vestnik Finun-ta, 2013: 2(10): 33–43 (in Russ.). EDN: QZQEET. Dvoynikova A. A., Karpov A. A. Analytical review of approaches to Russian text sentiment recognition. Informatsionno-upravlyayushchiye sistemy, 2020: 4 (107): 20–30 (in Russ.). DOI: 10.31799/1684-8853-2020-4-20-30. Zhuravlev A. L., Kitova D. A. Socio-Psychological Resources of the Development of Society in Digital Technology. Sotsiologicheskaya nauka i sotsial′naya praktika, 2020: 8: 2(30): 24–40 (in Russ.). DOI: 10.19181/snsp.2020.8.2.7301; EDN: VPJGAN. Ivanova I. A. Rol fenomena «obraz mira» v otechestvennoy psikhologii [The role of the "image of the world" phenomenon in Russian psychology]. Vestnik URAO, 2011: 1: 95–98 (in Russ.). EDN: NUFMVD. Kitova D. A., Zhuravlev A. L. Automated text analysis in psychology: State and prospects of world research. Psikhologicheskiy zhurnal. 2022: 43: 2: 105–115 (in Russ.). DOI: 10.31857/S020595920019417-4; EDN: DRRBOX. Krakov L. P., Ignat’yev M. V, Timiryasova A. V and others. Macroeconomics: textbook for universities. Moscow, IAEP, 2017: 336 (in Russ.). EDN: YTYYEI. Kuz′michуv S. M. Topical issues of understanding human capital and its role in modern economic processes. Molodoy uchenyy, 2017: 28(162): 63–64 (in Russ.). EDN: ZBFYXV. About the pressing problems of our lives and the interaction of regulators, business and citizens. A report on the results of a mass sociological study. Moscow, 2019. Accessed 16.10.19. URL: https://www.bankdelo.ru/news/100in1/pub/2570 (in Russ.). Psychological research in the Internet space: search engines, social networks, electronic databases. Moscow, IP RAN, 2020: 503 (in Russ.). DOI: 10.38098/soc.2020.89.1. Fukuyama F. Doveriye: sotsial′nyye dobrodeteli i put′ k protsvetaniyu [Trust: Social Virtues and the Path to Prosperity] Transl. from Eng by D. Pavlova, V. Kiryushchenko, M. Kolopotina Moscow, ACT: Yermak, 2004: 730 (in Russ.). Farooqui N., Ritika M., Saini A. Sentiment Analysis of Twitter Accounts using Natural Language Processing. International Journal of Engineering and Advanced Technology (IJEAT), 2019: 8: 3: 473–479. Hu Q. Twitter data in public administration: a review of recent scholarship. International Journal of Organization Theory & Behavior, 2019: 22: 2: 209–222. DOI: 10.1108/IJOTB–07–2018–0085. Inglehart R., Welzel S. Cultural Change and Democracy: The Human Development Sequence. Cambridge University Press, 2005: 334. Korobov M. Morphological analyzer and generator for Russian and Ukrainian languages. Communications in Computer and Information Science, 2015: 542: 330–342. Marechal C., Mikołajewski D., Tyburek K. et al. Survey on AI–Based Multimodal Methods for Emotion Detection. In Kołodziej J., González–Vélez H. (eds) High–Performance Modelling and Simulation for Big Data A Plications. Lecture Notes in Computer Science. Cham, Springer, 2019: 11400: 307–324. DOI: 10.1007/978–3–030–16272–6_11. Robila M., Robila S. A. A Plications of Artificial Intelligence Methodologies to Behavioral and Social Sciences. Journal of Child and Family Studies, 2019: 29: 2954–2966. DOI: 10.1007/s10826–019–01689–x. Xu H., Zhang N., Zhou L. Validity Concerns in Research Using Organic Data. Journal of Management, 2020: 46: 7: 1257–1274. DOI: 10.1177/0149206319862027. Zhuravlev A. L., Zinchenko Y. P., Kitova D. A. Trends in the Study of Cultural-Historical Phenomena on the Internet (based on a study of Russians’ attitudes towards money). Psychology in Russia: State of the Art, 2022: 15(1): 103–119. DOI: 10.11621/pir.2022.0107. Content Vestnik instituta sotziologii. 2023. Vol. 14. No. 4