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随机事件可以解释社交网络中意见的持续聚类和极化。

Stochastic events can explain sustained clustering and polarisation of opinions in social networks.

机构信息

CSIRO Oceans and Atmosphere, GPO Box 1538, Hobart, TAS, 7001, Australia.

Centre for Marine Socioecology, University of Tasmania, Hobart, TAS, Australia.

出版信息

Sci Rep. 2021 Jan 14;11(1):1355. doi: 10.1038/s41598-020-80353-7.

Abstract

Understanding the processes underlying development and persistence of polarised opinions has been one of the key challenges in social networks for more than two decades. While plausible mechanisms have been suggested, they assume quite specialised interactions between individuals or groups that may only be relevant in particular contexts. We propose that a more broadly relevant explanation might be associated with the influence of external events. An agent-based bounded-confidence model has been used to demonstrate persistent polarisation of opinions within populations exposed to stochastic events (of positive and negative influence) even when all interactions between individuals are noisy and assimilative. Events can have a large impact on the distribution of opinions because their influence acts synchronistically across a large proportion of the population, whereas an individual can only interact with small numbers of other individuals at any particular time.

摘要

理解导致观点极化的发展和持续的过程是社交网络 20 多年来的主要挑战之一。虽然已经提出了一些可行的机制,但它们假设了个体或群体之间相当特殊的相互作用,这些相互作用可能只在特定的背景下才相关。我们提出,一个更广泛适用的解释可能与外部事件的影响有关。基于代理的有界置信模型被用来证明即使在个体之间的所有相互作用都是嘈杂和同化的情况下,暴露于随机事件(正面和负面影响)的人群中观点仍然会持续极化。事件会对意见的分布产生重大影响,因为它们的影响在很大一部分人群中同步发生,而个体在任何特定时间只能与少数其他个体相互作用。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/47cb/7809277/b0e734ea3d3b/41598_2020_80353_Fig1_HTML.jpg

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