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Multidimensional polarization dynamics in US election data in the long term (2012–2020) and in the 2020 election cycle
Analyses of Social Issues and Public Policy ( IF 1.375 ) Pub Date : 2021-11-15 , DOI: 10.1111/asap.12278
Alejandro Dinkelberg 1, 2 , Caoimhe O'Reilly 1 , Pádraig MacCarron 1, 2 , Paul J. Maher 1 , Michael Quayle 1, 3
Affiliation  

We use a network-based method to explore bifurcation in the multidimensional opinion-based political identity structure from 2012 to 2020 in American National Election Studies data. We define polarization as ideological clustering which occurs when attitudes are linked or aligned across group-relevant dimensions. We identify relevant dimensions with a theory-driven approach and confirm them with the data-driven Boruta method, validating the importance of these items for self-reported political identity in these samples. To account for data sets having different sizes, we bootstrapped to obtain comparable samples. For each, a bipartite projection generates a network where edges represent similarity in responses between dyads. The data provide us with preidentified groups (Republicans and Democrats). We use them as our network communities and to calculate an edge-based polarization. Results show bifurcation progressively increasing, with a striking increase from 2016 to 2020. We visualize these identity-related shifts in opinion structure over time and discuss how polarization results from both between- and within-group dynamics. We apply a similar method to a smaller data set (N = 294) to explore short-term fluctuations before and after the 2020 election. Results suggest that between-group polarization is more evident after than before the election, because in-group opinion dynamics result in a more synchronized opinion-space for Republicans.

中文翻译:

美国选举数据在长期(2012-2020 年)和 2020 年选举周期中的多维极化动态

我们使用基于网络的方法来探索 2012 年至 2020 年美国国家选举研究数据中基于意见的多维政治身份结构中的分歧。我们将极化定义为当态度在与群体相关的维度上联系或一致时发生的意识形态聚类。我们用理论驱动的方法识别相关维度,并用数据驱动的 Boruta 方法确认它们,验证这些项目对于这些样本中自我报告的政治身份的重要性。为了说明具有不同大小的数据集,我们通过引导获得可比较的样本。对于每一个,二分投影会生成一个网络,其中边缘表示对子之间响应的相似性。这些数据为我们提供了预先确定的群体(共和党人和民主党人)。我们将它们用作我们的网络社区并计算基于边缘的极化。结果显示分歧逐渐增加,从 2016 年到 2020 年显着增加。我们可视化这些与身份相关的意见结构随时间的变化,并讨论两极分化如何由群体间和群体内的动态产生。我们将类似的方法应用于较小的数据集(N = 294) 来探索 2020 年大选前后的短期波动。结果表明,组间两极分化在选举后比选举前更明显,因为组内意见动态导致共和党人的意见空间更加同步。
更新日期:2021-11-15
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