Fraxanet Emma, Pellert Max, Schweighofer Simon, Gómez Vicenç, Garcia David
Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona 08018, Spain.
Chair for Data Science in the Economic and Social Sciences, University of Mannheim, Mannheim 68161, Germany.
PNAS Nexus. 2024 Jul 13;3(12):pgae276. doi: 10.1093/pnasnexus/pgae276. eCollection 2024 Dec.
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.
政治冲突是民主体制的一个基本要素，但如果变得过于激烈，也会威胁到民主体制的存在。这种情况尤其发生在大多数政治问题都沿着同一条主要断层线排列，将社会分裂为两个对立阵营的时候。在20世纪，主要断层线是由结构性冲突形成的，比如雇主与工人、中心与边缘等。但自那以后，这些传统的分裂已失去其解释力。我们没有对新的分裂进行理论化，而是提出了FAULTANA（断层线对齐网络分析）管道，这是一种计算方法，用于在有符号在线互动数据中揭示主要断层线。我们的方法能够量化不同在线辩论中普遍存在的对抗程度，以及每场辩论与主要断层线的对齐程度。这使得识别推动两极分化的关键问题成为可能，这些问题的特点是强烈的对抗和对齐。我们将我们的方法应用于美国的推特事实核查社区Birdwatch以及奥地利在线报纸DerStandard的讨论论坛的大规模数据集。我们发现，这两个在线社区都被分为两大群体，而且它们的分裂遵循政治身份和话题。此外，对于DerStandard，我们指出了加剧社会断层线从而推动两极分化的问题。我们还识别出了引发在线冲突但没有严格与那些分界线对齐的问题（例如新冠疫情）。我们的方法使我们能够构建一幅情感两极分化的时间分辨图，该图显示了在有争议的选举和事件期间，凝聚力和分裂性对对齐动态的单独贡献。
