User self-selection rather than algorithmic recommendations drives the consumption of politically segregated content on YouTube, though this finding about content navigation does not directly bound the algorithmic contribution to affective polarization through the out-group hostility channel.
Hosseinmardi et al. (2021) analyzed browser traces of 300,000 US YouTube users from 2016-2019 and found that consumption of far-right content was concentrated among users who already held those views and arrived via off-platform referrals.
YouTube recommendations rarely moved ordinary users toward extreme content, contradicting the algorithmic rabbit-hole hypothesis.
Barberá (2015) documented strong ideological clustering in Twitter following networks, establishing that the network-segregation baseline is primarily user-driven.
Is there a plausible alternative hypothesis that has not been considered or that would explain the facts at least as well?
Could the facts be jointly explained by a conjunction of weaker causes rather than a single dominant H?
Could the body of facts F itself be an artifact of selection, measurement, or reporting bias rather than a real phenomenon needing causal explanation?
Does H actually explain the full body of facts F, or only a salient subset?
Are the criteria used to judge H 'best' (scope, simplicity, mechanism, prior probability) appropriate for this domain, and are they applied consistently across the alternatives?