Models of Joint Dynamics of Opinions and Actions in Online Social Networks. Part III: Binary Models
- Authors: Gubanov D.A1, Novikov D.A1
-
Affiliations:
- Trapeznikov Institute of Control Sciences, Russian Academy of Sciences
- Issue: No 4 (2023)
- Pages: 14-27
- Section: Control in Social and Economic Systems
- URL: https://ogarev-online.ru/1819-3161/article/view/286636
- DOI: https://doi.org/10.25728/pu.2023.4.2
- ID: 286636
Cite item
Full Text
Abstract
Based on VKontakte data, we study the influence of various factors on the dynamics of opinions and actions both at the macro level (“public opinion”) and at the micro level (opinions and actions of individual agents). This paper concludes the multi-part study. Identification results are presented for binary models (threshold models and models with latent variables) that describe the dynamics of agents’ opinions and actions in a social network. These models are used to estimate the influence of various factors on agents’ opinions and actions (public opinion, the agent’s individual opinions and actions, the opinions and actions of the social environment, and the mechanisms of the agent’s trust in information sources and information content). Finally, linear models are compared with threshold models and qualitative findings of the multi-part study are drawn.
About the authors
D. A Gubanov
Trapeznikov Institute of Control Sciences, Russian Academy of Sciences
Email: dmitry.a.g@gmail.com
Moscow, Russia
D. A Novikov
Trapeznikov Institute of Control Sciences, Russian Academy of Sciences
Email: novikov@ipu.ru
Moscow, Russia
References
- Губанов Д.А., Новиков Д.А. Модели совместной динамики мнений и действий в онлайновых социальных сетях. Ч. 1. Исходные данные и первичный анализ // Проблемы управления. – 2023. – № 2. – С. 37–53. [Gubanov, D.A., Novikov, D.A. Models of Joint Dynamics of Opinions and Actions in Online Social Networks. Part I: Primary Data Analysis // Control Sciences. – 2023. – No. 2. – P. 31–45.]
- Губанов Д.А., Новиков Д.А. Модели совместной динамики мнений и действий в онлайновых социальных сетях. Ч. 2. Линейные модели // Проблемы управления. – 2023. – № 3. – С. 40–64. [Gubanov, D.A., Novikov, D.A. Models of Joint Dynamics of Opinions and Actions in Online Social Networks. Part II: Linear Models // Control Sciences. – 2023. – No. 3. – P. 31–54.]
- Gubanov, D.А. A Study of Formalizations of User Influence in Actional Model / Proceedings of the 13th International Conference "Management of Large-Scale System Development" (MLSD). – Moscow, 2020. – P. 1–5. – URL: https://ieeexplore.ieee.org/document/9247658.
- Губанов Д.А., Чхартишвили А.Г. Влиятельность пользователей и метапользователей социальной сети // Проблемы управления. – 2016. – № 6. – С. 12–17. [Gubanov, D.A., Chkhartishvili, A.G. An Actional Model of User Influence Levels in a Social Network // Automation and Remote Control. – 2015. – Vol. 76, iss 7. – P. 1282–1290.]
- Новиков Д.А. Модели динамики психических и поведенческих компонент деятельности в коллективном принятии решений // Управление большими системами. – 2020. – Вып. 85. – С. 206–237. [Novikov, D.A. Dynamics Models of Mental and Behavioral Components of Activity in Collective Decision-Making // Large-Scale Systems Control. – 2020. – Vol. 85. – P. 206–237. (In Russian)]
- Новиков Д.А., Бреер В.В., Рогаткин А.Д. Управление толпой: математические модели порогового коллективного поведения. – М.: ЛЕНАНД, 2016. – 168 с. [Novikov, D.A., Breer, V.V., Rogatkin, A.D. Upravlenie tolpoj: matematicheskie modeli porogovogo kollektivnogo povedeniya. – M.: LENAND, 2016. – 168 s. (In Russian)]
- Губанов Д.А., Новиков Д.А., Чхартишвили А.Г. Социальные сети: модели информационного влияния, управления и противоборства. 3-е изд., перераб. и дополн. – М.: МЦНМО, 2018. – 224 с. [Gubanov, D.A., Novikov, D.A., Chkhartishvili, A.G. Social Networks: Models of Information Influence, Control and Confrontation. Cham, Switzerland: Springer International Publishing, 2019. – 158 p.]
- Flache, A., Mäs, M., Feliciani, T., et al. Models of Social Influence: Towards the Next Frontiers // The Journal of Artificial Societies and Social Simulation. – 2017. – Vol. 20, no. 4. – doi: 10.18564/jasss.3521.
- Granovetter, M. Threshold Models of Collective Behavior // The American Journal of Sociology. – 1978. – Vol. 83, no. 6. – P. 1420–1443.
- Karimi, F., Holme, P. Threshold Model of Cascades in Empirical Temporal Networks // Physica A: Statistical Mechanics and Its Applications. – 2013. – Vol. 392, no. 16. – Р. 3476–3483.
- Kempe, D., Kleinberg, J., Tardos, E. Maximizing the Spread of Influence through a Social Network // Theory of Computing. – 2015. – Vol. 11, no. 4. – P. 105–147.
- Schelling, T. Micromotives and Macrobehaviour. – New York, London: Norton & Co Ltd, 1978. – 256 p.
- Semenov, A., Veremyev, A., Pasiliao, E.L., Boginski, V. Double-Threshold Models for Network Influence Propagation // Computational Data and Social Networks: 9th International Conference, CSoNet 2020. – Dallas, 2020. – P. 512–523.
- Brodersen, K., Ong, C., Stephan, K., Buhmann, J. The Balanced Accuracy and Its Posterior Distribution // Proceedings of the 20th International Conference on Pattern Recognition. – Istanbul, 2010. – P. 3121–3124.
- Elliott, R.J., Aggoun, L., Moore, J.B. Hidden Markov Models: Estimation and Control. – Luxembourg: Springer Science & Business Media, 2008.
- Koller, D., Friedman, N. Probabilistic Graphical Models: Principles and Techniques. – MIT Press, 2009.
- Никифоров И.В. Последовательное обнаружение изменения свойств временных рядов. – М.: Наука, 1983. – 199 с. [Nikiforov, I.V. Posledovatel'noe obnaruzhenie izmeneniya svojstv vremennyh ryadov. – M.: Nauka, 1983. – 199 s. (In Russian)]
- Дарховский Б.С., Бродский Б.Е. Апостериорное обнаружение момента «разладки» случайной последовательности // Теория вероятностей и ее применения. – 1980. – Т. 25. – Вып. 3. – С. 635–639. [Darhovskij, B.S., Brodskij, B.E. Aposteriornoe obnaruzhenie momenta «razladki» sluchajnoj posledovatel'nosti // Teoriya veroyatnostej i ee primeneniya. – 1980. – T. 25. – Vyp. 3. – S. 635–639. (In Russian)]
Supplementary files
