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A Noisy Opinion Formation Model with Two Opposing Mass Media

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Abstract

Processes of individual attitude formation and their macroscopic consequences have become an intriguing research topic, and agent-based models of opinion formation have been proposed to understand this phenomenon. This study conducted an agent-based simulation and examined the role of mass media in a noisy opinion formation process, where opinion heterogeneity is preserved by a weak intensity of assimilation and errors accompanying opinion modifications. In a computational model, agents conformed to their neighbours' opinions in social networks. In addition, each agent tended to be influenced by one of a two external agents with fixed opinions, that is, mass media that take opposite positions on an opinion spectrum. The simulation results demonstrated that a small probability of interactions with mass media reduces opinion heterogeneity even with extreme mass media position values. However, a large frequency of interactions with mass media increases opinion heterogeneity. Accordingly, intermediate assimilation strength achieves the least heterogeneous opinion distribution. The influence of mass media dampens the effects of network topology. Our simulation implies that mass media can play qualitatively different roles depending on their positions and intensity of influence.

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  • Hirofumi Takesue, 2021. "A Noisy Opinion Formation Model with Two Opposing Mass Media," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 24(4), pages 1-3.
    • Handle: RePEc:jas:jasssj:2020-173-3
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    1. Christopher A. Bail & Lisa P. Argyle & Taylor W. Brown & John P. Bumpus & Haohan Chen & M. B. Fallin Hunzaker & Jaemin Lee & Marcus Mann & Friedolin Merhout & Alexander Volfovsky, 2018. "Exposure to opposing views on social media can increase political polarization," Proceedings of the National Academy of Sciences, Proceedings of the National Academy of Sciences, vol. 115(37), pages 9216-9221, September.
    2. Galam, Serge & Jacobs, Frans, 2007. "The role of inflexible minorities in the breaking of democratic opinion dynamics," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 381(C), pages 366-376.
    3. Andreas Flache & Michael Mäs & Thomas Feliciani & Edmund Chattoe-Brown & Guillaume Deffuant & Sylvie Huet & Jan Lorenz, 2017. "Models of Social Influence: Towards the Next Frontiers," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 20(4), pages 1-2.
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    1. Takesue, Hirofumi, 2023. "Relative opinion similarity leads to the emergence of large clusters in opinion formation models," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 622(C).

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