Modul:   MAT959  Seminar in Data Science and Mathematical Modelling

Polarization and consensus: between online discussions and generative agents

Talk by Prof. Dr. David Garcia

Date: 07.11.24  Time: 12.15 - 13.45  Room: Y27H12

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. 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 factchecking 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. This way, we can track how this fault line manifests across issues and over time. Beyond social media, we are investigating how generative agents driven by Large Language Models can reach consensus or stay polarized and how this depends on group size and language understanding capabilities of the models.