Quantifying Polarization: A Comparative Study of Measures and Methods

TL;DR

This paper critically evaluates existing polarization measures, tests them with datasets, and introduces a new method based on Kleinberg's algorithm to better detect modes in ideological distributions on social media.

Contribution

It provides a comprehensive review of polarization measures and proposes an innovative adaptation of Kleinberg's burst detection algorithm for improved mode detection.

Findings

Identified strengths and weaknesses of five polarization measures

Demonstrated the effectiveness of the new method on real-world data

Enhanced understanding of ideological distribution patterns

Abstract

Political polarization, a key driver of social fragmentation, has drawn increasing attention for its role in shaping online and offline discourse. Despite significant efforts, accurately measuring polarization within ideological distributions remains a challenge. This study evaluates five widely used polarization measures, testing their strengths and weaknesses with synthetic datasets and a real-world case study on YouTube discussions during the 2020 U.S. Presidential Election. Building on these findings, we present a novel adaptation of Kleinberg's burst detection algorithm to improve mode detection in polarized distributions. By offering both a critical review and an innovative methodological tool, this work advances the analysis of ideological patterns in social media discourse.