printTowards the Decomposition and Forecasting of Social Mood of Russians:
a Neural Network Approach
Кaracharovskiy V.V.
Cand. Sci. (Econ.), Assoc. Prof., Faculty of Economic Sciences, Head of the Laboratory for Comparative Analysis of Post-Socialist Development, National Research University Higher School of Economics, Moscow, Russia. vvk@hse.ru
Larina U.S.
Research Intern, HSE University, Moscow, Russia uslarina@edu.hse.ru
Rezmeritsa A.A.
Research Intern, HSE University, Moscow, Russia aarezmeritsa@edu.hse.ru
This work was supported by the Russian Science Foundation, grant No. 24-28-01892
Кaracharovskiy V.V., Larina U.S., Rezmeritsa A.A. Towards the Decomposition and Forecasting of Social Mood of Russians: a Neural Network Approach. Sotsiologicheskie issledovaniya [Sociological Studies]. 2026. No 7. P. 60-75
Based on the neural network approach, the decomposition of the index of social mood in Russia for the period 2004–2024 was carried out and their dynamics for this period was modeled. The following issues are discussed: (a) the nature of the social mood’ shocks in Russia, based on their connection with the concepts of politics of memory, superpower and national idea; (b) the problem of ambivalence in the role of events and factors shaping social mood, (c) advantages and limitations of modeling and forecasting social mood based on neural networks approach. The possibility of decomposing the index of social mood into components of the economic well-being, comparative success in country development compared to the reference system of countries and epochs for Russians, and the factor of the Russian “superpower”, well approximated by indicators of military spending and the degree of openness of society to Western countries, is demonstrated. A neural network model is proposed that makes it possible to predict the dynamics of social mood based on universal (i.e., unrelated to the context of specific events) variables. The predictive power of the model is tested using the case of predicting the shock and subsequent dynamics of social mood in the period 2022–2024.
Casti J.L. (2010) Mood Matters: From Rising Skirt Lengths to the Collapse of World Powers. New York: Springer.
Cohen P.S. (1966) Social Attitudes and Sociological Enquiry. The British Journal of Sociology. Vol. 17. No. 4: 341–352.
Crone K. (2021) Foundations of a We-Perspective. Synthese. Vol. 198. No. 12: 11815–11832. DOI: 10.1007/s11229-020-02834-6; EDN: UCSMUN.
Funkenstein A. (1989) Collective Memory and Historical Consciousness. History and Memory. Vol. 1. No. 1: 5–26.
Gorshkov M.K., Barash R.E. (2024) Historical Memories of Russians Today (History of the XX century Russia in the Optics of Families’ Stories). Sotsiologicheskie issledovaniya [Sociological Studies]. No. 9: 125–137. DOI: 10.31857/S0132162524090119; EDN: MIDOFQ. (In Russ.)
Grimmer J. (2015) We Are All Social Scientists Now: How Big Data, Machine Learning, and Causal Inference Work Together. Political Science and Politics. Vol. 48. No. 1: 80–83.
Karaganov S.A., Malyutin P.N. (2025) The Russia’s Dream and Idea in the 21st Century. POLIS. Politicheskie issledovaniya [POLIS. Political Studies]. No. 4: 107–119. DOI: 10.17976/jpps/2025.04.09; EDN: UIRIIS. (In Russ.)
Kara-Murza S.G. (2012) Narodnoe hozyajstvo SSSR [The National Economy of the USSR]. Moscow: Algorithm.(In Russ.)
Kas’yanova K. (1994) O russkom nacional’nom haraktere [On the Russian National Character]. Moscow: INME. (In Russ.)
Kliucharev G.A. (2022) Ontological Problematics in Sociological Interpretation: To the Question Statement. Sotsiologicheskie issledovaniya [Sociological Studies]. No. 1: 5–16. DOI: 10.31857/S013216250017230-6; EDN: OFZZNJ. (In Russ.)
Latov Yu.V. (2024) Russians Between Futuroshock and Futuroeuphoria (Perception of the Future in the Context of Ideological Preferences). Sotsiologicheskie issledovaniya [Sociological Studies]. No. 12: 88– 101. DOI: 10.31857/S0132162524120087; EDN: NEFVGK. (In Russ.)
Malinetskiy G.G., Smolin V.S. et al. (2020) The Sociological Trajectory in AI Drafting: Challenges of Uncertainty. In: Designing the Future. Moscow: IPM im. M.V. Keldysha : 241–251. DOI: 10.20948/future-2020-22. EDN: QMTJQO. (In Russ.)
Mullainathan S., Spiess J. (2017) Machine Learning: An Applied Econometric Approach. The Journal of Economic Perspectives. Vol. 31. No. 2: 87–106
Myer T. D., Dahl A. (2017) Narrating Historical Injustice: Political Responsibility and the Politics of Memory. Political Research Quarterly. Vol. 70. No. 4: 905–917.
Rahn W.M., Kroeger B., Kite C.M. (1996) A Framework for the Study of Public Mood. Political Psychology. Vol. 17. No. 1: 29–58.
Ryan T.J. (2017) No Compromise: Political Consequences of Moralized Attitudes. American Journal of Political Science. Vol. 61. No. 2: 409–423.
Strohmaier D. (2021) Ontology, Neural Networks and the Social Sciences. Synthese. No. 1/2: 4775–4794. DOI: 10.1007/s11229-020-03002-6; EDN: FNCHVH.
Taylor R. (2010) Mood Swings. Science. Vol. 329. No. 5996: 1149.
Tikhonova N.Ye., Dudin I.V. (2024) Identity of Russians as a Consolidation Factor of Russian Society. Sotsiologicheskie issledovaniya [Sociological Studies]. No. 11: 18–33. DOI: 10.31857/S0132162524110023; EDN: NJKPJO. (In Russ.)
Toschenko Zh.T., Kharchenko S.V. (1996) Social’noe nastroenie [Social Mood]. Moscow: Academia. (In Russ.)
Toshchenko Zh. (2011) Sotsiologiya zhizni kak kontseptsiya issledovaniya sotsialnoy realnosti [Sociology of Life as a Concept of Social Reality Investigation]. Filosofskie nauki [Russian Journal of Philosophical Sciences]. No. 4: 26–43. EDN: NTBPBF. (In Russ.)
Varian H., Choi H. (2012) Predicting the Present with Google Trends. The Economic Record. Vol. 88. Spec. Iss. June: 2–9. DOI: 10.1111/j.1475-4932.2012.00809.x.