Romenskyy Maksym, Spaiser Viktoria, Ihle Thomas, Lobaskin Vladimir
Department of Life Sciences, Imperial College London, London SW7 2AZ, UK.
Department of Mathematics, Uppsala University, Uppsala 75106, Sweden.
R Soc Open Sci. 2018 Aug 1;5(8):171935. doi: 10.1098/rsos.171935. eCollection 2018 Aug.
Multiple countries have recently experienced extreme political polarization, which, in some cases, led to escalation of hate crime, violence and political instability. Besides the much discussed presidential elections in the USA and France, Britain's Brexit vote and Turkish constitutional referendum showed signs of extreme polarization. Among the countries affected, Ukraine faced some of the gravest consequences. In an attempt to understand the mechanisms of these phenomena, we here combine social media analysis with agent-based modelling of opinion dynamics, targeting Ukraine's crisis of 2014. We use Twitter data to quantify changes in the opinion divide and parametrize an extended bounded confidence XY model, which provides a spatio-temporal description of the polarization dynamics. We demonstrate that the level of emotional intensity is a major driving force for polarization that can lead to a spontaneous onset of collective behaviour at a certain degree of homophily and conformity. We find that the critical level of emotional intensity corresponds to a polarization transition, marked by a sudden increase in the degree of involvement and in the opinion bimodality.
最近，多个国家都经历了极端的政治两极分化，在某些情况下，这导致了仇恨犯罪、暴力事件的升级以及政治动荡。除了美国和法国备受讨论的总统选举外，英国脱欧公投和土耳其宪法公投也显示出极端两极分化的迹象。在受影响的国家中，乌克兰面临着一些最严重的后果。为了试图理解这些现象的机制，我们在此将社交媒体分析与基于主体的意见动态建模相结合，以2014年乌克兰危机为研究对象。我们使用推特数据来量化意见分歧的变化，并对扩展的有界置信度XY模型进行参数化，该模型提供了两极分化动态的时空描述。我们证明，情绪强度水平是两极分化的主要驱动力，在一定程度的同质性和一致性下，它可能导致集体行为的自发出现。我们发现，情绪强度的临界水平对应于一次两极分化转变，其特征是参与度和意见双峰性突然增加。
