“Fakes” in social networking media and modeling “fake infection”
DOI:
https://doi.org/10.46502/issn.2712-8024/2021.4.1Keywords:
fake news, mathematical modeling, social networks, vertical distribution, identificationAbstract
The growth of dynamism, the complexity of relationships in social networks requires a systematic approach, the development of mathematical models for forecasting and the identification of fake news in social networks. Otherwise, it is difficult to resist media misinformation, fake news. The problem is urgent, there are more and more opportunities for exchanging "viral" and fake messages in social networks, and we poorly implement monitoring, identifying fake risks. Social networks so far do not allow reliably distinguishing lies from news from aggregator. The purpose of the work is to predict and analyze the system-phase pattern of the spread of fakes in the space of social interactions. "Fakes" are deliberately false, intended for manipulation. In recent years, they are easily distributed in social networks. In the work by methods of the theory of ordinary differential equations, their qualitative analysis, the above problem was full investigated. The study was conducted under assumptions: remote distributors are not allowed to participate in the transmission of fakes; an adult population susceptible to fakes has a constant birth rate; propagation can occur "vertically," wherein the transmission mechanism is introduced into the model by appropriate assumptions about the proportion of susceptible and distributors. The problem is fully investigated (solvability, unambiguity, phase patterns of stable behavior). The work will be useful in the practical identification and prediction of the influence of fake news.
References
Aral, S., & Eckles, D. (2019) Protecting elections from social media manipulation. Science, No. 365, pp. 858–861.
Busenberg, S.N., Cooke, K.L., & Pozio, M.A. (1983) Analysis of a model of a vertically transmitted disease. Journal of Mathematical Biology, 17(3), pp. 305-329.
Domagoj, B., & Volarevi?, M. (2018) New Problems, Old Solutions? A Critical Look on the Report of the High Level Expert Group on Fake News and On-Line Disinformation. Media Studies, ? 9(17), pp. 106-117. DOI: 10.22363/2313-1438-2018-20-3-447-460.
Feldman, L. (2007) The news about comedy: Young audiences, The Daily Show, and evolving notions of journalism. Journalism, No. 8, pp. 406-427.
Golovatskaya, O. (2019) The meaning and origin of the term "Fake news". Communicology, 7(2), pp. 139-152. DOI 10.21453/2311-3065-2019-7-2-139-152.
Kaziev, V., Kaziev, K., & Kazieva, B. (2017) Fundamentals of legal informatics and informatization of legal systems: a textbook. 2nd ed. M.: University textbook: INFRA-M, 336p.
Lazer, D., Baum, M., Benkler, Y., Berinsky, A., Greenhill, K., et al. (2018). The science of fake news. Science, No. 359, pp. 1094-1096.
Persily, N. (2017) Can democracy survive the Internet? J. Democr., No. 28, pp. 63-76.
Sukhodolov, A., & Bychkova, A. (2017) "Fake News" as a phenomenon of modern media space: concept, types, purpose, countermeasures. Questions of the Theory and Practice of Journalism, 6(2), pp. 143-169. DOI 10.17150/2308-6203.2017.6(2).143-169.
Shu, K., Sliva, A., Wang, S., Tang, J., & Liu, H. (2021) Fake News Detection on Social Media: A Data Mining Perspective. Arxiv.org. URL: https://arxiv.org/pdf/1708.01967.pdf (Date of the application: 15.10.2020).
Stella, M., Ferrara, E., & Domenico, M.D. (2018) Bots increase exposure to negative and inflammatory content in online social systems. Proc. Natl. Acad. Sci. USA, No. 115, pp. 12435-12440.
Tretyakov, A., Filatova, O., Zhuk, D., Gorlushkina, N., & Puchkovskaya, A. (2018) Method of determining Russian-language fake news using elements of artificial intelligence. Intern. J. of Open Information Technol., 6(12), pp. 99-105.
Vosoughi, S., Roy, D., & Aral, S. (2018) The spread of true and false news online. Science, No. 359, pp. 1146–1151.