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arXiv 2108.02643physics.soc-phcond-mat.stat-mechnlin.AO
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社交网络中的意见极化

Opinion polarisation in social networks

Nadia Loy, Matteo Raviola, Andrea Tosin

摘要:

在本文中,我们提出了一个玻尔兹曼型动力学描述的意见形成的社交网络,它考虑到一般的连接分布的个人。我们认为意见交换过程的灵感来自Sznajd模型和相关的简化,但我们不假设个人互动的一个定期的格子。相反,我们用统计学的方法来描述社会网络的结构,假设一个人的联系人数量决定了他的观点到达并影响另一个人的观点的概率。从动力学描述的系统,我们研究的平均意见的演变,在那里我们找到精确的分析条件下,一个\textit{polarisation switch}的意见,即一个变化的迹象之间的初始和渐近平均意见,发生。特别是,我们表明,初始意见和连接的个人之间的非零相关性是必要的,以观察极化开关。最后,我们通过社会网络上的随机意见交换过程的蒙特卡罗模拟验证我们的分析结果。

英文摘要:

In this paper, we propose a Boltzmann-type kinetic description of opinion formation on social networks, which takes into account a general connectivity distribution of the individuals. We consider opinion exchange processes inspired by the Sznajd model and related simplifications but we do not assume that individuals interact on a regular lattice. Instead, we describe the structure of the social network statistically, assuming that the number of contacts of a given individual determines the probability that their opinion reaches and influences the opinion of another individual. From the kinetic description of the system, we study the evolution of the mean opinion, whence we find precise analytical conditions under which a \textit{polarisation switch} of the opinions, i.e. a change of sign between the initial and the asymptotic mean opinions, occurs. In particular, we show that a non-zero correlation between the initial opinions and the connectivity of the individuals is necessary to observe polarisation switch. Finally, we validate our analytical results through Monte Carlo simulations of the stochastic opinion exchange processes on the social network.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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