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Author(s):
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Ole Kelm, Tim Neumann, Maike Behrendt, Markus Brenneis, Katharina Gerl, Stefan Marschall, Florian Meißner, Stefan Harmeling, Gerhard Vowe, Marc Ziegele
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Title:
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How algorithmically curated online environments influence users’ political polarization: Results from two experiments with panel data
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Published:
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Article, October 2023
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Keyword(s):
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Algorithms, Polarization, Online experiments, Filter bubble, Panel data, Germany
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Abstract:
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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.
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Bib entry:
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[BibTeX]
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Download:
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[PDF]
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