How algorithmically curated online environments influence users’ political polarization: Results from two experiments with panel data

• Published in: Computers in human behavior reports 2023-12, Vol.12, p.100343, Article 100343
• Main Authors: Kelm, Ole, Neumann, Tim, Behrendt, Maike, Brenneis, Markus, Gerl, Katharina, Marschall, Stefan, Meißner, Florian, Harmeling, Stefan, Vowe, Gerhard, Ziegele, Marc
• Format: Article
• Language: English
• Subjects: Algorithms; Filter bubble; Germany; Online experiments; Panel data; Polarization
• ISSN: 2451-9588; 2451-9588
• DOI: 10.1016/j.chbr.2023.100343

Social media platforms are often accused of disproportionally exposing their users to like-minded opinions, thereby fueling political polarization. However, empirical evidence of this causal relationship is inconsistent at best. One reason could be that many previous studies were unable to separate the effects caused by individual exposure to like-minded content from the effects caused by the algorithms themselves. This study presents results from two quasi-experiments in which participants were exposed either to algorithmically selected or randomly selected arguments that were either in line or in contrast with their attitudes on two different topics. The results reveal that exposure to like-minded arguments increased participants’ attitude polarization and affective polarization more intensely than exposure to opposing arguments. Yet, contrary to popular expectations, these effects were not amplified by algorithmic selection. Still, for one topic, exposure to algorithmically selected arguments led to slightly stronger attitude polarization than randomly selected arguments. •Algorithms are often accused of exposing their users to like-minded opinions, thereby fueling political polarization.•We created online environments that adapt political arguments in line with or in contrast to users' preferences over time.•We tested the effects of algorithmic curation on users' polarization in a three-wave experimental panel survey.•Exposure to like-minded arguments lead to stronger political polarization than exposure to opposing arguments.•Algorithmically curation did not amplify these effects, but had small direct effects on attitude polarization.