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.
@article{arxiv.2501.07473,
title = {Quantifying Polarization: A Comparative Study of Measures and Methods},
author = {Edoardo Di Martino and Matteo Cinelli and Roy Cerqueti and Walter Quattrociocchi},
journal= {arXiv preprint arXiv:2501.07473},
year = {2025}
}