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Affective polarization is more than mere antagonism as it appears whennegative interactions happen mostly across political divisions. Research inpolarization usually assumes a given definition of political divisions orconflates polarization and disagreement as the same phenomenon. Leveraging onnovel data sources of positive and negative online interactions, we present amethod to computationally discover the fault lines of an online community withminimal assumptions on the dividing issues. This enables us to unpack twofactors of polarization: Antagonism, which is the general prevalence ofhostility in online interaction, and Alignment, which captures how negativerelations exist across groups (divisiveness) while positive interactions arecontained within (cohesiveness). We apply our approach to Birdwatch, a US-basedTwitter fact-checking community, and to the discussion forums of DerStandard,an Austrian online newspaper. Our results reveal that both communities aredivided into two large groups and that their separation follows politicalidentities and topics. We can identify issues across various combinations ofantagonism and alignment in DerStandard, evidencing that these two metrics arenot equivalent. Our methods provide a time-resolved picture that illustratesthe separate contribution of cohesiveness and divisiveness and the role ofcontroversial elections and events in the dynamics of alignment.

Unpacking polarization: Antagonism and Alignment in Signed Networks of Online Interaction