Abstract

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.

Highlights

  • The growth of relationships in social networks requires the development of intelligent systems and mathematical models of fake news in social networks

  • Remember the problem: popular news aggregators and social networks compete for your attention, sometimes they can manipulate the viewer

  • Episodes of fake infection can be described by more complex mathematical models and mechanisms

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Summary

Introduction

The growth (in volume, dynamism, complexity, connectivity) of relationships in social networks (hereinafter referred to as social networks) requires the development of intelligent systems and mathematical models (forecasting) of fake news in social networks (see, for example, Tretyakov et al, 2018; Golovatskaya, 2019). The Google-request "fake news" produces more than a billion pages, and the request "fake news science article" - 361 million. There is already a science of fake news (Lazer, Baum, Benkler et al, 2018), the problem of forecasting is all the more relevant, and the more possible it is to exchange "viral" and anonymous messages on social networks. Almost poorly implemented social network monitoring, identification of fake risks and actors

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