Open Access"Fakes" in social networking media and modeling "fake infection"
Author(s) -
С. В. Михайлова
Publication year - 2021
Publication title -
personality and society
Language(s) - English
Resource type - Journals
ISSN - 2712-8024
DOI - 10.46502/issn.2712-8024/2021.4.1
Subject(s) - identification (biology) , misinformation , social media , computer science , dynamism , population , internet privacy , transmission (telecommunications) , social network (sociolinguistics) , news aggregator , data science , computer security , telecommunications , world wide web , sociology , botany , physics , demography , quantum mechanics , biology
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.