Political conflict is an essential element of democratic systems, but can also threaten their existence if it becomes too intense. This happens particularly when most political issues become aligned along the same major fault line, splitting society into two antagonistic camps. In the 20th century, major fault lines were formed by structural conflicts, like owners vs workers, center vs periphery, etc. But these classical cleavages have since lost their explanatory power. Instead of theorizing new cleavages, we present the FAULTANA (FAULT-line Alignment Network Analysis) pipeline, a computational method to uncover major fault lines in data of signed online interactions. Our method makes it possible to quantify the degree of antagonism prevalent in different online debates, as well as how aligned each debate is to the major fault line. This makes it possible to identify the wedge issues driving polarization, characterized by both intense antagonism and alignment. We apply our approach to large-scale data sets of Birdwatch, a US-based Twitter fact-checking community and the discussion forums of DerStandard, an Austrian online newspaper. We find that both online communities are divided into two large groups and that their separation follows political identities and topics. In addition, for DerStandard, we pinpoint issues that reinforce societal fault lines and thus drive polarization. We also identify issues that trigger online conflict without strictly aligning with those dividing lines (e.g. COVID-19). Our methods allow us to construct a time-resolved picture of affective polarization that shows the separate contributions of cohesiveness and divisiveness to the dynamics of alignment during contentious elections and events.

翻译：政治冲突是民主制度的基本要素，但若冲突过于激烈，也可能威胁其存续。当多数政治议题沿着同一主要断裂线对齐，社会分裂为两个对立阵营时，这种情形尤为突出。20世纪的主要断裂线由结构性冲突（如资方与劳方、中心与边缘等）构成，但这些经典的社会分野已失去解释力。我们并未致力于构建新的分野理论，而是提出FAULTANA（断裂线对齐网络分析）流程——一种从符号化在线交互数据中揭示主要断裂线的计算方法。该方法可量化不同在线辩论中的对抗程度，以及各辩论与主要断裂线的对齐程度，从而识别出以激烈对抗与高度对齐为特征的驱动极化的楔子议题。我们将此方法应用于美国推特事实核查社区Birdwatch及奥地利在线报纸DerStandard讨论区的大规模数据集。研究发现，这两个在线社区均分裂为两大群体，其分野遵循政治身份与议题划分。针对DerStandard，我们进一步确定了强化社会断裂线进而加剧极化的具体议题，同时识别出引发在线冲突却未严格对齐这些分界线的议题（如COVID-19）。通过该方法，我们构建了情感极化随时间演变的动态图景，揭示了选举争端等敏感事件中内聚力与分裂性对极化对齐动力学的独立贡献。