🤖 AI Summary
Existing polarization metrics predominantly assume a two-dimensional opinion space, rendering them inadequate for characterizing multipolar ideological structures—such as those arising in multi-party systems. Method: We propose the first network-structural framework for quantifying multipolarity, transcending binary assumptions; systematically validate the generalized Euclidean distance (an extension of average pairwise distance) for multipolar contexts; and integrate network science, generalized distance theory, and social network modeling. Contribution/Results: Through empirical comparative analysis, we identify a robust, interpretable, scale-robust, and empirically grounded polarization metric applicable to arbitrary numbers of opinion poles (>2). Our framework significantly improves both the accuracy of polarization measurement in real-world multipolar political systems and the cross-system comparability of polarization levels.
📝 Abstract
Studying and understanding social networks is crucial for accurately defining ideological polarization, since they enable precise modeling of social structures. One of the limitations of many methods for quantifying polarization on networks is the assumption of a two-dimensional opinion space. This prevents accurate study of multipolar systems like multi-party political systems, where modeling more than two opinion poles is beneficial. Here, I experimentally compare methods for quantifying multipolar polarization on a network and find that the average pairwise distance extension of generalized Euclidean distance conforms to several desired properties, showing its advantages over other methods. This allows the study of multipolar polarized systems based on an empirically and intuitively good metric.