Multiway Alignment of Political Attitudes

2024-07-31

arXiv.org

AI Summary

Existing research on political attitudes predominantly examines pairwise issue associations, failing to capture individuals’ systematic stance structures across multiple issues. Method: We propose a “multi-issue joint alignment” metric—extending mutual information via information theory—to quantify how much an individual’s attitude on a single issue reveals information about their configuration across multiple issues. We empirically estimate third-order and higher joint alignment using voting behavior modeling, time-series analysis, and longitudinal ANES survey data. Contribution/Results: We find that party identification exhibits a structural reinforcement of multi-issue consistency over time; multi-way alignment is more stable in parliamentary systems but displays significant group-level heterogeneity; in the U.S., joint alignment has risen steadily, reflecting deepening partisan polarization. This work introduces a scalable, high-order analytical framework for studying political polarization and the evolution of attitudinal structure.

Technology Category

• Knowledge Representation and Reasoning: Preferences
• Natural Language Processing: Sentiment Analysis, Stylistic Analysis, and Argument Mining
• Machine Learning: Learning Preferences or Rankings

Application Category

• Social Networks and Social Media: Media and governance, opinion dynamics, filter bubbles, polarization
• Security and Privacy: Large-scale security measurements
• User Modeling, Personalization and Recommendation: Studies of user behavior, including longitudinal effects of personalized systems

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.

Problem

• Political Views
• Multi-topic Analysis
• Inter-topic Relationship

Innovation

• Political View Inference
• Multi-Issue Stance Detection
• Partisan Affiliation Analysis