  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.
