Affective polarization is more than mere antagonism as it appears when negative interactions happen mostly across political divisions. Research in polarization usually assumes a given definition of political divisions or conflates polarization and disagreement as the same phenomenon. Leveraging on novel data sources of positive and negative online interactions, we present a method to computationally discover the fault lines of an online community with minimal assumptions on the dividing issues. This enables us to unpack two factors of polarization: Antagonism, which is the general prevalence of hostility in online interaction, and Alignment, which captures how negative relations exist across groups (divisiveness) while positive interactions are contained within (cohesiveness). We apply our approach to Birdwatch, a US-based Twitter fact-checking community, and to the discussion forums of DerStandard, an Austrian online newspaper. Our results reveal that both communities are divided into two large groups and that their separation follows political identities and topics. We can identify issues across various combinations of antagonism and alignment in DerStandard, evidencing that these two metrics are not equivalent. Our methods provide a time-resolved picture that illustrates the separate contribution of cohesiveness and divisiveness and the role of controversial elections and events in the dynamics of alignment.

翻译：情感极化不仅仅是简单的对抗，它通常表现为负面互动主要跨越政治分歧而发生。极化研究通常假定政治分歧的既定定义，或将极化与分歧混为一谈。利用正面和负面在线互动的新型数据源，我们提出了一种计算方法，在最小化对分歧议题假设的前提下，自动发现在线社区的断裂线。这使我们能够解析极化的两个因素：对抗（即在线互动中敌意的普遍盛行）与对齐（即负面关系跨越群体分布（分裂性）而正面互动局限在群体内部（内聚性）的程度）。我们将该方法应用于美国Twitter事实核查社区Birdwatch以及奥地利在线报纸DerStandard的讨论论坛。结果表明，两个社区均分裂为两大群体，且其分野遵循政治身份与议题。我们在DerStandard中识别出多种对抗与对齐组合下的议题，证明这两个指标并不等价。我们的方法提供了时间分辨的动态图景，揭示了内聚性与分裂性的独立贡献，以及有争议的选举和事件在对齐动力学中的作用。