While a growing body of research examines the effect of deplatforming, the blanket banning of certain types of content and users, on social media discourse, comparatively little research examines the effect of replatforming, when social media companies remove such blanket bans. We exploit such a policy change, YouTube’s June 2nd decision to stop removing content denying the validity of the 2020 US Presidential Election, to examine the effect of replatforming content. Using data from YouTube, Facebook, Telegram conspiracy groups, and Google, we find no evidence that YouTube's policy change increased demand for or supply of election denial content over the short term. These results are consistent across all platforms and three different methodologies: regression discontinuity models, structural topic models, and a Bayesian structural time series model. This suggests that replatforming alone, when conducted after the effective marginalization of the targeted content, has minimal short-term effects on the spread of previously-prohibited content

Does the URL referral structure of websites lead users into ‘rabbit holes’ of unreliable content? Past work suggests algorithmic recommender systems on sites like YouTube lead users to view more unreliable content. However, websites without algorithmic recommender systems have financial and political motivations to influence the movement of users, potentially creating browsing rabbit holes. We address this gap using browser telemetry that captures referrals to a large sample of domains rated as reliable or unreliable information sources. Our results suggest the incentives for unreliable sites to retain and monetise users create rabbit holes. After landing on an unreliable site, users are very likely to be referred to another page on the site. Further, unreliable sites are better at retaining users than reliable sites. We find less support for political motivations. While reliable and unreliable sites are largely disconnected from one another, the probability of traveling from one unreliable site to another is relatively low. Our findings indicate the need for additional focus on site-level incentives to shape traffic moving through their sites.

Societal-scale data is playing an increasingly prominent role in social science research; examples from research on geopolitical events include questions on how emergency events impact the diffusion of information or how new policies change patterns of social interaction. Such research often draws critical inferences from observing how an exogenous event changes meaningful metrics like network degree or network entropy. However, as we show in this work, standard estimation methodologies make systematically incorrect inferences when the event also changes the sparsity of the data.