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Complex System Modeling and Simulation 2021, 1(2): 109-121 https://doi.org/10.23919/CSMS.2021.0008
Open Access | Issue | Published: 30 June 2021

Public Opinion Dissemination with Incomplete Information on Social Network: A Study Based on the Infectious Diseases Model and Game Theory

Show Author's Information Bin Wu( ),  Ting Yuan,  Yuqing Qi,  Min Dong
School of Economics and Management, Nanjing Tech University, Nanjing 211816, China
Keywords:
social network, incomplete information, Deffuant model, evolutionary game
Cite this article:
Wu B, Yuan T, Qi Y, et al. Public Opinion Dissemination with Incomplete Information on Social Network: A Study Based on the Infectious Diseases Model and Game Theory. Complex System Modeling and Simulation, 2021, 1(2): 109-121. https://doi.org/10.23919/CSMS.2021.0008
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Abstract Full text About this article

Given the fragmentation of public opinion dissemination and the lag of network users’ cognition, the paper analyzes public opinion dissemination with incomplete information, which can provide reference for us to control and guide the spread of public opinion. Based on the derivative and secondary radiation of public opinion dissemination with incomplete information, the Susceptible-Susceptible-Infected-Recovered-Recovered-Infected (SSIR R-I) model is proposed. Given the interaction between users, the Deffuant opinion dynamics model and evolutionary game theory are introduced to simulate the public opinion game between dissemination and immune nodes. Finally, the numerical simulation and results analysis are given. The results reveal that the rate of opinion convergence significantly affects disseminating public opinion, which is positively correlated with the promotion effect of the dissemination node and negatively correlated with the suppression effect of the immune node of public opinion dissemination. Derivative and secondary radiations have different effects on public opinion dissemination in the early stage, but promote public opinion dissemination in the later stage. The dominant immune nodes have an apparent inhibitory effect on the spread of public opinion; nevertheless, they cannot block the dissemination of public opinion.


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About this article

Public Opinion Dissemination with Incomplete Information on Social Network: A Study Based on the Infectious Diseases Model and Game Theory

Show Author's information Bin Wu( ), Ting Yuan, Yuqing Qi, Min Dong
School of Economics and Management, Nanjing Tech University, Nanjing 211816, China

Abstract

Given the fragmentation of public opinion dissemination and the lag of network users’ cognition, the paper analyzes public opinion dissemination with incomplete information, which can provide reference for us to control and guide the spread of public opinion. Based on the derivative and secondary radiation of public opinion dissemination with incomplete information, the Susceptible-Susceptible-Infected-Recovered-Recovered-Infected (SSIR R-I) model is proposed. Given the interaction between users, the Deffuant opinion dynamics model and evolutionary game theory are introduced to simulate the public opinion game between dissemination and immune nodes. Finally, the numerical simulation and results analysis are given. The results reveal that the rate of opinion convergence significantly affects disseminating public opinion, which is positively correlated with the promotion effect of the dissemination node and negatively correlated with the suppression effect of the immune node of public opinion dissemination. Derivative and secondary radiations have different effects on public opinion dissemination in the early stage, but promote public opinion dissemination in the later stage. The dominant immune nodes have an apparent inhibitory effect on the spread of public opinion; nevertheless, they cannot block the dissemination of public opinion.

Keywords: social network, incomplete information, Deffuant model, evolutionary game

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Publication history

Received: 19 March 2021
Accepted: 16 April 2021
Published: 30 June 2021
Issue date: June 2021

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© The author(s) 2021

Acknowledgements

This work was supported by the National Social Science Foundation of China (No. 20BGL025) and the Postgraduate Practice Innovation Program of Jiangsu Province (No. SJCX20_0316).

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The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).

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