2025/01/13 by Di Martino, Edoardo, Cinelli, Matteo, Cerqueti, Roy +1 · 1 citation
#Computers and Society (cs.CY) #FOS: Computer and information sciences #FOS: Physical sciences #Physics and Society (physics.soc-ph) #Social and Information Networks (cs.SI)
paper · doi:10.48550/arxiv.2501.07473
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.