Information wars in the contemporary world and simulation of news dissemination

Cover Page

Cite item

Full Text

Open Access Open Access
Restricted Access Access granted
Restricted Access Subscription or Fee Access

Abstract

The paper considers information wars that are part of modern hybrid conflicts. They were analyzed using computer models that implement the process of information dissemination in social communities. The typology of the most relevant and cited tools made possible to find an effective algorithm for implementing the authors’ agent-oriented model that takes into account individual characteristics of people and allows differentiated assessment of the impact of information messages only on a certain group. Within the framework of computational experiments, the speed of information dissemination in the constructed digital twin of a social network was estimated depending on the change in the number of opinion leaders and the number of initially informed agents, as well as on the decrease in the average level of reputation of network agents. The instrument designed may be used separately, as well as along within the complex models, including demographic and economic components.

Full Text

Restricted Access

About the authors

I. V. Losik

“Zvezda” (“The Star”) TV and Radio Company of the Armed Forces of the Russian Federation; “Heirs of the Winners” Fund for the Preservation of the Cultural and Historical Memory of War Heroes; Lomonosov Moscow State University

Author for correspondence.
Email: iralosiknews@mail.ru

presenter of evening news “Itogi Dnya” (“Results of the Day”) of “Zvezda” (“The Star”) TV and Radio Company of the Armed Forces of the Russian Federation; President of “Heirs of the Winners” Fund for the Preservation of the Cultural and Historical Memory of War Heroes; graduate student of the Higher School of Public Audit, Faculty of Lomonosov Moscow State University

Russian Federation, Moscow

S. V. Sidorenko

The Russian Academy of Sciences

Email: sidor@presidium.ras.ru

Department of Scientific & Methodological Supervision and Expert Activity

Russian Federation, Moscow

M. Y. Sidorenko

The Russian Academy of Sciences

Email: myusidorenko@pran.ru

Department of Scientific & Information Activity of the RAS and Interaction with the Scientific & Educational Community; Scientific & Publishing Council

Russian Federation, Moscow

A. R. Bakhtizin

Central Economics and Mathematics Institute, Russian Academy of Sciences; Lomonosov Moscow State University

Email: albert.bakhtizin@gmail.com

Corresponding Member of the Russian Academy of Sciences

Russian Federation, Moscow

References

  1. Акопов А. С., Бекларян Л. А., Бекларян А. Л. (2021). Мультисекторная модель ограниченного соседства: сегрегация агентов и оптимизация характеристик среды // Математическое моделирование. Т. 33. № 11. С. 95– 114. doi: 10.20948/mm-2021-11-06 [Akopov A. S., Beklaryan L. A., Beklaryan A. L. (2021). Multisector bounded-neighborhood model: Agent segregation and optimization of environment’s characteristics. Matematicheskoe Modelirovanie, 33, 11, 95–114. doi: 10.20948/mm-2021-11-06 (in Russian); Akopov A. S., Beklaryan L. A., Beklaryan A. L. (2022). Multisector bounded-neighborhood model: Agent segregation and optimization of environment’s characteristics. Mathematical Models and Computer Simulations, 14, 3, 503–515. doi: 10.1134/S2070048222030024 (in English).]
  2. Губанов Д. А. (2021). Модели и методы информационного влияния и управления в активных сетевых структурах. Дис. … уч. степ. докт. техн. наук. М.: ИПУ РАН. 307 с. [Gubanov D. A. (2021). Models and methods of information influence and management in active network structures. Dissertation for the degree of Doctor of Technical Sciences Moscow: Institute of Control Sciences RAS. 307 p. (in Russian).]
  3. Мачуева Д. А., Ажмухамедов И. М. (2018). Моделирование процесса информационного взаимодействия в социальных системах // Системы управления, связи и безопасности. № 2. С. 18–39. Режим доступа: http://sccs.intelgr.com/archive/2018-02/02-Machueva.pdf [Machueva D. A., Azhmuhamedov I. M. (2018). Modeling the information interaction process social systems. Systems of Control, Communication and Security, 2, 18–39. Available at: http://sccs.intelgr.com/archive/2018-02/02-Machueva.pdf (in Russian).]
  4. Adämmer P., Schüssler R. A. (2019). Forecasting the equity premium: Mind the news. Review of Finance. Forthcoming. SSRN: 3370424. doi: 10.2139/ssrn.3370424
  5. Arruda H. F., Cardoso F. M., Arruda G. F., Hernandez A. R., Costa L. D., Moreno Y. (2022). Modelling how social network algorithms can influence opinion polarization. Information Sciences, 588, 265–278. ISSN: 0020-0255. doi: 10.1016/j.ins.2021.12.069
  6. Bass F. (1969). A new product growth for model consumer durables. Management Science, 15 (5), 215–227. doi: 10.1287/mnsc.15.5.215
  7. Beklaryan A. L., Beklaryan L. A., Akopov A. S. (2022). Implementation of the deffuant model within the FLAME GPU framework. Advances in Systems Science and Applications, 21, 4, 87–99. doi: 10.25728/assa.2021.21.4.1161
  8. Bruine de Bruin W., Parker A. M., Strough J. (2020). Age differences in reported social networks and well-being. Psychology and Aging, March, 35 (2), 159–168. doi: 10.1037/pag0000415
  9. Cai M., Luo H., Meng X. et al. (2022). Influence of information attributes on information dissemination in public health emergencies. Humanities Social Sciences Communications, 9, 257. doi: 10.1057/s41599-022-01278-2
  10. Calomiris C. W., Mamaysky H. (2019). How news and its context drive risk and returns around the world. Journal of Financial Economics, 133, 2, 299–336. doi: 10.1016/j.jfineco.2018.11.009
  11. Gonçalves B., Perra N., Vespignani A. (2011). Modeling users’ activity on twitter networks: Validation of Dunbar’s number. PLoS One, 6 (8), e22656. doi: 10.1371/journal.pone.0022656 Available at: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3149601
  12. Hong Y., Jiang F., Meng L., Xue B. (2022). Forecasting inflation with economic narratives and machine learning. SSRN: https://ssrn.com/abstract=4175749 or doi: 10.2139/ssrn.4175749
  13. Kumar N. (2019). Information diffusion and summarization in social networks. A thesis submitted to Indian institute of technology Hyderabad in partial fulfillment of the requirements for the degree of doctor of philosophy. Hyderabad: Indian Institute of Technology. Available at: https://core.ac.uk/reader/224956800
  14. Kumar P., Sinha A. (2021). Information diffusion modeling and analysis for socially interacting networks. Social Network Analysis and Mining, 11, 1. doi: 10.1007/s13278-020-00719-7
  15. Kumar S., Saini M., Goel M. et al. (2021). Modeling information diffusion in online social networks using a modified forest-fire model. Journal of Intelligent Information Systems, 56, 355–377. doi: 10.1007/s10844-020-00623-8
  16. Larsen V. H., Thorsrud L. A., Zhulanova J. (2021). News-driven inflation expectations and information rigidities. Journal of Monetary Economics, 117, 507–520. doi: 10.1016/j.jmoneco.2020.03.004
  17. Li M., Wang X., Gao K., Zhang S. (2017). A survey on information diffusion in online social networks: Models and methods. Information, 8, 118. doi: 10.3390/info8040118
  18. Liu L., Qu B., Chen B., Hanjalic A., Wang H. (2018). Modeling of information diffusion on social networks with applications to WeChat. Physica A: Statistical Mechanics and its Applications, 496, 318–329. doi: 10.1016/j.physa.2017.12.026
  19. Pond T., Magsarjav S., South T., Mitchell L., Bagrow J. P. (2020). Complex contagion features without social reinforcement in a model of social information flow. Entropy, 22 (3), 265.
  20. Qiang Z., Pasiliao E. L., Zheng Q. P. (2019). Model-based learning of information diffusion in social media networks. Applied Network Science, 4. Article number: 111. doi: 10.1007/s41109-019-0215-3
  21. Rand W., Herrmann J., Schein B., Vodopivec N. (2015). An agent-based model of urgent diffusion in social media. Journal of Artificial Societies and Social Simulation, 18 (2), 1. doi: 10.18564/jasss.2616 or Available at: http://jasss.soc.surrey.ac.uk/18/2/1.html
  22. Rand W., Rust R. T. (2011). Agent-based modeling in marketing: Guidelines for rigor. International Journal of Research in Marketing, 28 (3), 181–193. doi: 10.1016/j.ijresmar.2011.04.002
  23. Razaque A., Rizvi S., Khan M. J., Almiani M., Rahayfeh A. A. (2022). State-of-art review of information diffusion models and their impact on social network vulnerabilities. Journal of King Saud University — Computer and Information Sciences, 34, 1, 1275–1294. ISSN: 1319–1578. doi: 10.1016/j.jksuci.2019.08.008 or Available at: https://www.sciencedirect.com/science/article/pii/S131915781930388X
  24. Röchert D., Cargnino M., Neubaum G. (2022). Two sides of the same leader: An agent-based model to analyze the effect of ambivalent opinion leaders in social networks. Journal of Computational Social Science, 5, 1159–1205. doi: 10.1007/s42001-022-00161-z
  25. Schawe H., Beiró M. G., Alvarez-Hamelin J.I. et al. (2023). Understanding who talks about what: Comparison between the information treatment in traditional media and online discussions. Sci. Reports, 13, 3809. doi: 10.1038/s41598-023-30367-8
  26. Vosoughi S., Roy D., Aral S. (2018). The spread of true and false news online. Science, Mar, 359, 6380, 1146–1151. doi: 10.1126/science.aap9559
  27. Wang Y., Li G. (2018). The spreading of information in online social networks through cellular automata. Complexity, 1–9. doi: 10.1155/2018/1890643

Supplementary files

Supplementary Files
Action
1. JATS XML
Download
2. Fig. 1. Typology of information dissemination models

Download (207KB)
Indexing metadata
3. Fig. 2. Conceptual diagram of the model operation

Download (298KB)
Indexing metadata
4. Fig. 3. Growth rate of informed agents: base case and scenario 1 (abscissa axis – model time steps, ordinate axis – growth rate, %)

Download (116KB)
Indexing metadata
5. Fig. 4. Growth rate of informed agents: base case and scenario 2 (abscissa axis – model time steps, ordinate axis – growth rate, %)

Download (110KB)
Indexing metadata
6. Fig. 5. Growth rate of informed agents: base case and scenario 3 (abscissa axis – model time steps, ordinate axis – growth rate, %)

Download (108KB)
Indexing metadata
7. Fig. 6. Results of the Twitter network study in terms of analyzing the dynamics of discussions of the most attention-grabbing social processes in 2020–2021 (the vertical axis is the number of unique network participants using the corresponding hashtags plotted along one of the horizontal axes) Source: Schawe et al., 2023.

Download (164KB)
Indexing metadata

Copyright (c) 2024 Russian Academy of Sciences