Putting ‘filter bubble’ effects to the test: evidence on the polarizing impact of ideology-based news recommendation from two experiments in Germany and the US

Algorithmic news recommender systems (NRS) are present in many digital platforms. A decade after Eli Pariser introduced the infamous ‘filter bubble’ hypothesis, empirical evidence challenges the assumption that recommendation algorithms predominantly create homogeneous opinion environments. Studies indicate that algorithmic platform use may amplify users’ political polarization. Whether the link between platform use and polarization can be causally explained by ideological news filtering, however, is still an unanswered question as rigid causal designs to test the notion of ‘filter bubble’ effects are still largely lacking. To fill this gap, we conducted two experimental studies in Germany (n = 1,786) and the U.S. (n = 1,306) with running NRS selecting news items based on the political orientation and political interest of its users. For both national contexts, results indicate that an NRS with a bias towards users’ political preferences increases ideological polarization among politically moderate individuals, supporting the notion of ‘filter bubble’ effects for this group. No such pattern could be found for affective polarization. Yet, in the German data, affective polarization among moderate users was reduced by a politically balanced NRS (as compared to a randomized news diet), while the same NRS increased affective polarization of politically extreme participants. ... mehrWe discuss the democratic implications of these findings against the backdrop of increasing digital news consumption.