Online Political Flashmob: the Case of 632305222316434
https://doi.org/10.23947/2587-8999-2019-1-1-17-28
Abstract
This paper considers the case of online flashmob 63230522231643, which was intended to show the indignation of Russian opposition to the contested parliamentary elections in 2011. The expression of indignation lasted for 47 days. The organizers called on all who believed that the elections had been rigged to enter the aforementioned number anywhere on the internet so that it could be indexed by search engines. The assumption was that the number could hardly be expected to appear in any context other than the flashmob. Thus, using the Google search engine and counting its appearances, anyone would be able to estimate the extent or severity of indignation, which was the political opposition members thought was as very widespread. However, the flashmob mobilized only a small number of participants and failed to achieve its political goals. At the same time, it turned out to be a rare example of the spread of information in a world that was sparse, that is, considering only those individuals whose political attitudes and dispositions to online activity made them available for participation, we get a social network with relatively few edges, which is a sub-network of a denser and larger network of users.
Methods. The paper presents empirical data on the daily number of flashmob-related tweets and presents a mathematical model to describe the dynamics of a flashmob on Twitter and compare the results of modeling with empirical data for flashmob 63230522231643.
Results. The model correctly represents and explains some important features of the dynamics of a flashmob. To explain the other features would require more a more complex model and building and verifying such a model would call for more abundant empirical data.
About the Authors
Alexander P. Petrov
Keldysh Institute of Applied Mathematics
(4, Miusskaya Sq., Moscow, Russian Federation)
Russian Federation
Russian Federation
Petrov Alexander P., Dr.Sci. (Math), Leading Researcher at Keldysh Institute of Applied
Mathematics (4, Miusskaya Sq., Moscow, Russian Federation)
Sergei A. Lebedev
Keldysh Institute of Applied Mathematics
(4, Miusskaya Sq., Moscow, Russian Federation)
Russian Federation
Russian Federation
Lebedev Sergei A., Postgraduate Student at Keldysh Institute of Applied Mathematics (4,
Miusskaya Sq., Moscow, Russian Federation)
References
1. https://spichechka.livejournal.com/221514.html (last accessed 2019-06-07).
2. http://www.wikireality.ru/wiki/632305222316434 (last accessed 2019-06-07).
3. Online database of smart questions https://db.chgk.info, https://kand.info,
4. https://bazavoprosov.ru/, https://ww.w.chgk7.ru, https://www.pdf.baza-voprosov.ru, https://zaba.ru/
5. (the list of URLs may be incomplete; last accessed 2019-06-07).
6. D. J. Daley, D. G. Kendall. Stochastic rumors // Journal of the Institute of Mathematics and
7. its Applications, vol. 1, pp. 42–55, 1964.
8. D. P. Maki, M. Thompson. Mathematical Models and Applications, Prentice-Hall,
9. Englewood Cliffs, NJ, USA, 1973.
10. Chen, G., Shen, H., Ye, T., Chen, G., Kerr, N. A kinetic model for the spread of rumor in
11. emergencies // Discrete Dyn. Nat. Soc. 2013, 1–8 (2013).
12. Isea, R., Mayo-Garcıa, R. Mathematical analysis of the spreading of a rumor among different
13. subgroups of spreaders // Pure Appl. Math. Lett. 2015, 50–54 (2015).
14. Huo, L., Huang, P., Guo, C. Analyzing the dynamics of a rumor transmission model with
15. incubation // Discrete Dyn. Nat. Soc. 2012, 1–21 . Article ID 328151. (2012).
16. Kawachi, K. Deterministic models for rumor transmission // Nonlinear Anal. Real World Appl. 9(5), 1989–2028 (2008).
17. Dickinson, R.E., Pearce, C.E.M. Rumours, epidemics, and processes of mass action: synthesis and analysis // Math. Comput. Model. 38(11–13), 1157–1167 (2003).
18. Mikhailov, A.P. and Marevtseva, N.A. Models of information warfare // Mathematical models and computer simulations, 4(3), pp.251-259 (2012).
19. Kereselidze, N. Combined continuous nonlinear mathematical and computer models of the Information Warfare // International Journal of Circuits, Systems and Signal Processing.Vol.12, p.220-228 (2018).
20. Kereselidze, N., Mathematical and Computer Models of Non-Permanent Information Warfare // WSEAS Transactions on Systems 18 (Art. #8), 73-80 (2019).
21. Petrov A., Proncheva O. Modeling Propaganda Battle: Decision-Making, Homophily, and Echo Chambers. In: Ustalov D., Filchenkov A., Pivovarova L., Žižka J. (eds) Artificial Intelligence and Natural Language. AINL 2018. Communications in Computer and Information Science, vol 930. Springer. P. 197-209. DOI: 10.1007/978-3-030-01204-5_19 (2018).
22. Mikhailov A. P., Petrov A. P., Proncheva O. G. Modeling the effect of political polarization on the outcome of propaganda battle // Computational mathematics and information technologies. — 2017. — No 1. — P. 65–81. doi:10.23947/2587-8999-2017-1-1-65-81.
23. Kozitsin, I. V., Belolipetskii, A. A. Opinion convergence in the Krasnoshchekov model // The Journal of Mathematical Sociology, 43(2), 104-121 (2019).
24. Kozitsin, I. V. Generalization of Krasnoshchekov’s model for the case of a decomposable matrix of social interactions // Mathematical Models and Computer Simulations, 10(4), 398-406 (2018).
25. Chkhartishvili A.G., Gubanov D.A., Novikov D.A. Social Networks: Models of information influence, control and confrontation. - Springer, 2019, 158 p.
26. Chkhartishvili, A., Kozitsin, I. Binary Separation Index for Echo Chamber Effect Measuring. In 2018 Eleventh International Conference» Management of large-scale system development» MLSD (pp. 1-4). IEEE (2018).
27. Doerr, B., Fouz, M., Friedrich, T.: Why rumors spread so quickly in social networks // Commun. ACM 55(6), 70–75 (2012).
28. Zhang, Y., Tang, C., Weigang, L.: Cooperative and competitive dynamics model for information propagation in online social networks // J. Appl. Math. 2014, 1–12 . Article ID 610382 (2014).
29. Akhtyamova L., Ignatov A., Cardiff J. A Large-Scale CNN Ensemble for Medication Safety Analysis. In: Frasincar F., Ittoo A., Nguyen L., Métais E. (eds) Natural Language Processing and Information Systems. NLDB 2017. Lecture Notes in Computer Science, vol 10260. Springer, Cham. pp. 247-253/. https://link.springer.com/chapter/10.1007/978-3-319-59569-6_29 (2017).
30. Boldyreva A., Sobolevskiy O., Alexandrov M., Danilova V. Creating collections of descriptors of events and processes based on Internet queries // Proc. of 14-th Mexican Intern. Conf. on Artif. Intell. (MICAI-2016). - Springer Cham, LNAI, vol. 10061 (chapter 26), p. 303-314, //https://link.springer.com/chapter/10.1007/978-3-319-62434-1_26 (2016).
31. Boldyreva A., Alexandrov M., Koshulko O., Sobolevskiy O. Queries to Internet as a tool for analysis of the regional police work and forecast of the crimes in regions // Proc. of 14-th Mexican Intern. Conf. on Artif. Intell. (MICAI-2016). - Springer Cham, LNAI, v. 10061 (chapter 25), p. 290-302, // https://link.springer.com/chapter/10.1007/978-3-319-62434-1_25 (2016).
32. Rocca, N.M. Internet security facing Jihadist propaganda in Europe: the last challenge for society? // Cahiers des IFRE, No 4. (2017).
33. Korobiichuk, I., Hryshchuk, R., Mamarev, V., Okhrimchuk, V. and Kachniarz, M. Cyberattack Classificator Verification // International Conference on Diagnostics of Processes and Systems (pp. 402-411). Springer, Cham (2017)
34. Mishra, B.K. and Prajapati, A. Modelling and simulation: cyber war. Procedia Technology, 10, pp.987-997 (2013).
35. A.P. Mikhailov, A. P. Petrov, G. B. Pronchev and O. G. Proncheva. Modeling a Decrease in Public Attention to a Past One-Time Political Event // Doklady Mathematics, Vol. 97, No. 3, pp. 247–249. ISSN 1064-5624. doi: 10.1134/S1064562418030158 (2018).
Review
For citations:
Petrov A.P., Lebedev S.A. Online Political Flashmob: the Case of 632305222316434. Computational Mathematics and Information Technologies. 2019;3(1). (In Russ.) https://doi.org/10.23947/2587-8999-2019-1-1-17-28
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