Multiway Alignment of Political Attitudes (2408.00139v2)

Abstract: The related concepts of partisan belief systems, issue alignment, and partisan sorting are central to our understanding of politics. These phenomena have been studied using measures of alignment between pairs of topics, or how much individuals' attitudes toward a topic reveal about their attitudes toward another topic. We introduce a higher-order measure that extends the assessment of alignment beyond pairs of topics by quantifying the amount of information individuals' opinions on one topic reveal about a set of topics simultaneously. Applying this approach to legislative voting behavior shows that parliamentary systems typically exhibit similar multiway alignment characteristics, but can change in response to shifting intergroup dynamics. In American National Election Studies surveys, our approach reveals a growing significance of party identification together with a consistent rise in multiway alignment over time.

• The paper presents a multiway alignment measure that leverages entropy and mutual information to quantify higher-order political attitude interdependencies.
• It develops consensus partitions using normalized mutual information and a null model to capture multidimensional ideological divides.
• Empirical analyses across Finnish and American datasets reveal shifts in alignment due to crises and growing partisan polarization.

Multiway Alignment of Political Attitudes

The paper "Multiway Alignment of Political Attitudes," authored by Letizia Iannucci, Ali Faqeeh, Ali Salloum, Ted Hsuan Yun Chen, and Mikko Kivelä, introduces a higher-order measure to quantify the alignment of political attitudes across multiple topics simultaneously. Unlike traditional pairwise metrics, this method captures the richer structure of interdependencies among sets of political topics, offering a more comprehensive understanding of ideological divides.

Theoretical Framework and Methodology

The core contribution of this paper is the development of a multiway alignment measure based on entropy and mutual information. The measure quantifies how much information an individual's opinion on one topic reveals about their opinions on a set of topics. This approach extends beyond pairwise associations and addresses higher-order constraints in political belief systems.

The notion of a consensus partition is central to this methodology. A consensus partition groups individuals based on their shared opinions across multiple topics, thus capturing the multidimensional nature of political alignment. The multiway alignment measure is then obtained by averaging normalized mutual information (NMI) between each individual topic and the consensus partition defined by the remaining topics. The paper also normalizes these scores using a null model to account for alignment that can be attributed to chance.

Empirical Validation and Findings

The authors apply this measure to various datasets, including legislative voting behavior in the Finnish Parliament and the U.S. House, as well as public opinion surveys from the American National Election Studies (ANES). The results reveal the paper's utility in both elite and public contexts.

1. Finnish Parliament: The analysis of roll-call votes before and during the COVID-19 pandemic unveils significant changes in multiway alignment. Specifically, the onset of the pandemic led to a marked decrease in multiway alignment, reflecting an increase in bipartisan cooperation and a shift in parliamentary focus towards urgent crisis management topics like health and transportation.
2. Finnish Twitter: The paper of retweet networks during the 2019 and 2023 Finnish political elections shows an increase in the overall alignment of political discussions. The 2023 data particularly highlights a shift towards a more ideologically driven political landscape, with higher-order constraints becoming more pronounced across various topics.
3. U.S. Public Opinion (ANES): The time-series analysis from 2004 to 2020 indicates a growing influence of party identification on public opinion. Over time, the American public exhibits increasing multiway alignment, suggesting a more polarized populace where attitudes on various issues are increasingly interdependent and aligned along partisan lines.

Implications and Future Directions

The introduction of a multiway alignment measure has significant implications both theoretically and practically. Theoretically, it provides a robust framework for studying the complexity of political belief systems, capturing higher-order constraints that pairwise measures miss. Practically, this measure can be employed to better understand societal polarization and the formation of partisan identities in different socio-political contexts.

Future research could extend this framework to other forms of social networks and political systems, exploring its utility in more diverse settings. Additionally, the development of more sophisticated null models could further refine the measure's accuracy, helping to disentangle genuine ideological alignment from random associations.

In conclusion, the paper provides a novel and versatile tool for the quantitative analysis of political attitudes across multiple dimensions. The multiway alignment measure offers nuanced insights into the structure of political belief systems, paving the way for future studies on polarization, ideological divides, and the evolving nature of political discourse.