Multidimensional polarization dynamics in US election data in the long term (2012–2020) and in the 2020 election cycle

Alejandro Dinkelberg,Pádraig Maccarron,Michael Quayle,Paul J Maher,Caoimhe O'Reilly

doi:10.1111/asap.12278

Abstract

AbstractWe 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.

Highlights

The mean cut size or mean degree of polarization goes from 13.4% to 12.28% and from 2016 to 2020, it changes to 8.84%
Note: Important items in the American National Election Studies, selected by the Boruta method, to determine people who selfidentified as Republicans and Democrats
Our research adds weight to these claims by demonstrating a growing ideological distance between Republicans and Democrats over the last 8 years using a multidimensional approach to mapping identity (DellaPosta et al, 2015) with bipartite network methods similar to those pioneered by Breiger and colleagues (Breiger et al, 2014)

Summary

Introduction

We use a network-based method to explore the multidimensional attitudinal structure of political party identifiers over an 8-year time period and measure polarization as the ideological distance between political party supporters (Dinkelberg et al, 2021). We apply the same approach to generate the attitude-based networks for each time point (2012, 2016, and 2020) in order to capture the trend in attitude-based polarization among these samples of American people in election years over this decade.

Results

Conclusion