Lewis-Sigler Institute for Integrative Genomics, Program in Quantitative and Computational Biology, Princeton University, Princeton, NJ 08544;
Earth System Analysis, Potsdam Institute for Climate Impact Research, Potsdam 14473, Germany.
Proc Natl Acad Sci U S A. 2021 Dec 14;118(50). doi: 10.1073/pnas.2104194118.
Although spatial polarization of attitudes is extremely common around the world, we understand little about the mechanisms through which polarization on divisive issues rises and falls over time. We develop a theory that explains how political shocks can have different effects in different regions of a country depending upon local dynamics generated by the preexisting spatial distribution of attitudes and discussion networks. Where opinions were previously divided, attitudinal diversity is likely to persist after the shock. Meanwhile, where a clear precrisis majority exists on key issues, opinions should change in the direction of the predominant view. These dynamics result in greater local homogeneity in attitudes but at the same time exacerbate geographic polarization across regions and sometimes even within regions. We illustrate our theory by developing a modified version of the adaptive voter model, an adaptive network model of opinion dynamics, to study changes in attitudes toward the European Union (EU) in Ukraine in the context of the Euromaidan Revolution of 2013 to 2014. Using individual-level panel data from surveys fielded before and after the Euromaidan Revolution, we show that EU support increased in areas with high prior public support for EU integration but declined further where initial public attitudes were opposed to the EU, thereby increasing the spatial polarization of EU attitudes in Ukraine. Our tests suggest that the predictive power of both network and regression models increases significantly when we incorporate information about the geographic location of network participants, which highlights the importance of spatially rooted social networks.
尽管世界各地的态度空间极化现象极为普遍，但我们对导致极化现象随时间上升和下降的机制知之甚少。我们提出了一种理论，该理论解释了政治冲击如何根据态度的预先存在的空间分布和讨论网络产生的当地动态，在一个国家的不同地区产生不同的影响。在以前存在分歧的地方，态度的多样性在冲击后可能会持续存在。同时，在关键问题上存在明确的危机前多数派的情况下，意见应该朝着主流观点的方向转变。这些动态导致态度的局部同质性增加，但同时加剧了地区之间甚至地区内部的地理极化。我们通过开发自适应投票者模型（一种意见动态的自适应网络模型）的修改版本来说明我们的理论，以研究 2013 年至 2014 年乌克兰的欧洲一体化运动（Euromaidan Revolution）背景下，人们对欧盟的态度变化。我们使用在欧洲一体化运动之前和之后进行的调查中的个人层面的面板数据，表明在公众对欧盟一体化高度支持的地区，对欧盟的支持增加了，但在最初的公众态度反对欧盟的地区，支持率进一步下降，从而增加了乌克兰对欧盟态度的空间极化。我们的测试表明，当我们将网络参与者的地理位置信息纳入网络和回归模型时，两者的预测能力都显著提高，这突出了具有空间根源的社交网络的重要性。
