Modelling the Influence of Social Media on Collective Opinion

T. Ohnishi
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Abstract

When we divide our present society into real and cyber worlds, there exist no clear data on how the public attitude or opinion is formed and on what sort of opinion distribution is realized in the cyber world. We propose a methodology for the model calculation with which we can compare the observation of the public opinion formed under the environment of social media in the cyber world. The public viewpoint or the opinion about a certain matter, together with the standpoint of the information provided by the social media, can not be given by some discrete values, but they make fuzzy distributions within certain ranges of opinion around certain central values. With the assumption that the variation of the public opinion originates from the emotional contagion induced by the contact of the public with the social media, and that the force realized by this contagion is given in terms of the common area of such fuzzy distributions of the public opinion and the information on the social media, we derived an equation of motion for the variation of public opinion. By further assuming that the information diffuses from a top toward a bottom of a ramified tree structure of node networks, we exemplified some characteristic patterns of the distribution of collective opinion including the effect of echo-chamber, which are realized under certain input spectra of the information on the social media. Moreover, by using the observed data for the 2016 USA President election as an input, we made clear that the reversal of the approval rating might possibly occur between the political right and left wings in so far as the response character of supporters to the social media differ depending on the political situation of the public.
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社交媒体对集体意见的影响建模
当我们把现在的社会分为现实世界和网络世界时,没有明确的数据表明公众的态度或意见是如何形成的,以及在网络世界中如何实现意见分配。我们提出了一种模型计算的方法,我们可以用它来比较网络世界中社交媒体环境下形成的民意观察。公众对某一事物的观点或意见,以及社交媒体所提供信息的立场,并不是由一些离散的值给出的,而是围绕着某些中心值在一定的意见范围内做出模糊的分布。假设民意的变异源于公众与社交媒体的接触所引起的情绪传染,并且这种传染所实现的力是根据这种民意模糊分布与社交媒体上的信息的共同面积给出的,我们推导出了民意变异的运动方程。通过进一步假设信息在节点网络的分枝树结构中由上向下扩散,我们举例说明了包括回音室效应在内的集体意见分布的一些特征模式,这些模式是在社交媒体上信息的一定输入谱下实现的。此外,我们以2016年美国总统大选的观察数据为输入,明确了由于支持者对社交媒体的反应特征因公众政治形势的不同而不同,因此在政治右翼和左翼之间可能会出现支持率的逆转。
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