I. V. Kozitsin, A. G. Chkhartishvili, A. M. Marchenko, D. O. Norkin, S. D. Osipov, I. A. Uteshev, V. L. Goiko, R. V. Palkin, M. G. Myagkov, “Modelling Russian users' political preferences”, Mat. Model., 31:8 (2019), 3–20; Math. Models Comput. Simul., 12:2 (2020), 185

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Matematicheskoe modelirovanie, 2019, Volume 31, Number 8, Pages 3–20
DOI: https://doi.org/10.1134/S023408791908001X (Mi mm4100)

This article is cited in 21 scientific papers (total in 21 papers)

Modelling Russian users' political preferences

I. V. Kozitsin^ab, A. G. Chkhartishvili^b, A. M. Marchenko^a, D. O. Norkin^a, S. D. Osipov^a, I. A. Uteshev^a, V. L. Goiko^c, R. V. Palkin^c, M. G. Myagkov^dc

^a Moscow Institute of Physics and Technology, Dolgoprudny, Russian Federation
^b Trapeznikov Institute of Control Sciences RAS, Moscow, Russian Federation
^c Tomsk State University, Tomsk, Russian Federation
^d University of Oregon, Eugene, USA

Full-text PDF (423 kB) Citations (21)

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DOI: https://doi.org/10.1134/S023408791908001X

Abstract: In this paper, we present two machine learning models that can predict Russian VKontakte users' political preferences. They imply operationing at the users-level. We consider thoroughly its different applications; one of them is public opinion monitoring. To demonstrate it, we test them on the sample of 22 mil of Russian users of age. Finally, we retrieve two estimations of public opinion. In case we value the outcome of the 2018 Presidential election by these estimations, we get MAE of 12 and 19.4 percent correspondingly. Moreover, one of the algorithms finds correctly the first three places. Another prominent utility relates to the calibration of opinion dynamics models where we can use scores generated by the machine learning algorithms to estimate users' opinions numerically.

Keywords: users' political leaning prediction, online social networks analysis, opinion dynamics, machine learning, public opinion.

Funding agency	Grant number
Russian Foundation for Basic Research	18-29-22042_мк

Received: 21.01.2019
Revised: 21.03.2019
Accepted: 08.04.2019

English version:
Mathematical Models and Computer Simulations, 2020, Volume 12, Issue 2, Pages 185–194
DOI: https://doi.org/10.1134/S2070048220020088

Bibliographic databases:

Document Type: Article

Language: Russian

Citation: I. V. Kozitsin, A. G. Chkhartishvili, A. M. Marchenko, D. O. Norkin, S. D. Osipov, I. A. Uteshev, V. L. Goiko, R. V. Palkin, M. G. Myagkov, “Modelling Russian users' political preferences”, Mat. Model., 31:8 (2019), 3–20; Math. Models Comput. Simul., 12:2 (2020), 185–194

Citation in format AMSBIB

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\by I.~V.~Kozitsin, A.~G.~Chkhartishvili, A.~M.~Marchenko, D.~O.~Norkin, S.~D.~Osipov, I.~A.~Uteshev, V.~L.~Goiko, R.~V.~Palkin, M.~G.~Myagkov

\paper Modelling Russian users' political preferences

\jour Mat. Model.

\yr 2019

\vol 31

\issue 8

\pages 3--20

\mathnet{http://mi.mathnet.ru/mm4100}

\crossref{https://doi.org/10.1134/S023408791908001X}

\elib{https://elibrary.ru/item.asp?id=38487764}

\transl

\jour Math. Models Comput. Simul.

\yr 2020

\vol 12

\issue 2

\pages 185--194

\crossref{https://doi.org/10.1134/S2070048220020088}

Linking options:

https://www.mathnet.ru/eng/mm4100

https://www.mathnet.ru/eng/mm/v31/i8/p3

This publication is cited in the following 21 articles:

Nikolay Belotelov, Fedor Loginov, Communications in Computer and Information Science, 1913, Advances in Optimization and Applications, 2024, 147
Vitaliy Kashpur, Alexey Baryshev, Galina Serbina, Alexander Gubanov, Ilya Demeshkin, “Possibilities of analyzing the network connectivity of ideological and monothematic radical online communities on VKontakte”, Sociology: methodology, methods, mathematical modeling (Sociology: 4M), 29:57 (2024), 42
Vladislav N. Gezha, Ivan V. Kozitsin, “The Effects of Individuals' Opinion and Non-Opinion Characteristics on the Organization of Influence Networks in the Online Domain”, Computers, 12:6 (2023), 116
Ivan V. Kozitsin, Alexander V. Gubanov, Eduard R. Sayfulin, Vyacheslav L. Goiko, “A nontrivial interplay between triadic closure, preferential, and anti-preferential attachment: New insights from online data”, Online Social Networks and Media, 34-35 (2023), 100248
Kozitsin I.V., “Formal Models of Opinion Formation and Their Application to Real Data: Evidence From Online Social Networks”, J. Math. Sociol., 46:2 (2022), 120–147
Alexander Petrov, Lecture Notes in Networks and Systems, 503, Cybernetics Perspectives in Systems, 2022, 35
D. A. Gubanov, “Methods for analysis of information influence in active network structures”, Autom. Remote Control, 83:5 (2022), 743–754
Anna Karpova, Aleksei Savelev, Alexander Vilnin, Sergey Kuznetsov, “Method for Detecting Far-Right Extremist Communities on Social Media”, Social Sciences, 11:5 (2022), 200
A. V. Glazkova, O. V. Zakharova, A. V. Zakharov, N. N. Moskvina, T. R. Enikeev, A. N. Hodyrev, V. K. Borovinskiy, I. N. Pupysheva, “Detecting mentions of green practices in social media based on text classification”, Model. Anal. Inform. Sist., 29:4 (2022), 316–332
Jiefan Zhu, Yiping Yao, Wenjie Tang, Haoming Zhang, “Dynamic Parameter Calibration Framework for Opinion Dynamics Models”, Entropy, 24:8 (2022), 1112
I. V. Kozitsin, “Opinion dynamics of online social network users: a micro-level analysis”, J. Math. Sociol., 2021
A. G. Chkhartishvili, “The problem of finding the median preference of individuals in a stochastic model”, Autom. Remote Control, 82:5 (2021), 853–862
Asmit Kumar Singh, Chirag Jain, Jivitesh Jain, Rishi Raj Jain, Shradha Sehgal, Tanisha Pandey, Ponnurangam Kumaraguru, Proceedings of the 2021 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, 2021, 193
L. G. Byzov, D. A. Gubanov, I. V. Kozitsin, A. G. Chkhartishvili, “A Perfect Politician for Social Networks: an Approach to Analyzing Ideological Preferences of Users”, Autom Remote Control, 82:9 (2021), 1614
D. A. Gubanov, “Vliyanie v sotsialnykh setyakh: varianty formalizatsii”, UBS, 85 (2020), 51–71
A. Peshkovskaya, M. Myagkov, “Eye gaze patterns of decision process in prosocial behavior”, Front. Behav. Neurosci., 14 (2020), 525087
Alexander Petrovich Mikhailov, Alexander Phoun Chzho Petrov, Gennadi Borisovich Pronchev, Olga Gennadevna Proncheva, Proceedings of 22nd Scientific Conference “Scientific Services & Internet – 2020”, Proceedings of 22nd Scientific Conference “Scientific Services & Internet – 2020”, 2020, 462
Alexander Chkhartishvili, 2020 13th International Conference “Management of large-scale system development” (MLSD), 2020, 1
Ivan V. Kozitsin, Alexander G. Chkhartishvili, 2020 13th International Conference “Management of large-scale system development” (MLSD), 2020, 1
Tatiana Babkina, Anna Sedush, Olga Menshikova, Mikhail Myagkov, Communications in Computer and Information Science, 1340, Advances in Optimization and Applications, 2020, 145

Citing articles in Google Scholar: Russian citations, English citations
Related articles in Google Scholar: Russian articles, English articles

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