Abstract: Social networks scaffold the diffusion of information on social media. Much attention has been given to the spread of true vs. false content on social media, including the structural differences between their diffusion patterns. However, much less is known about how platform interventions on false content alter the diffusion of such content. In this work, we estimate the causal effects of a novel fact-checking feature, Community Notes, adopted by Twitter (now X) to solicit and vet crowd-sourced fact-checking notes for false content. An important aspect of this feature is its use of a bridging-based decision algorithm whereby fact-checking notes are shown only if they are seen as broadly informative and helpful by users from across the political spectrum. To estimate the causal effect of bridging-based fact-checking, we gather detailed time series data for 40,000 posts for which notes have been proposed and use synthetic control methods to produce counterfactual estimates of a range of diffusion-based outcomes. We find that attaching fact-checking notes significantly reduced the reach of and engagement with false content. In reducing reach, we observe that diffusion trees for fact-checked content are less deep, but not less broad, than synthetic control estimates for non-fact-checked content with similar reach. This finding contrasts notably with differences between false vs. true content, where false information diffuses farther, but with structural patterns that are otherwise indistinguishable from those of true information, conditional on reach.

Bio: Johan Ugander is an Associate Professor at Stanford University in the Department of Management Science & Engineering, within the School of Engineering. His research develops algorithmic and statistical frameworks for analyzing social networks, social systems, and other large-scale social and behavioral data. Prior to joining the Stanford faculty he was a post-doctoral researcher at Microsoft Research Redmond 2014-2015 and held an affiliation with the Facebook Data Science team 2010-2014. He obtained his Ph.D. in Applied Mathematics from Cornell University in 2014. His awards include a NSF CAREER Award, a Young Investigator Award from the Army Research Office (ARO), three Best Paper Awards (2012 ACM WebSci Best Paper, 2013 ACM WSDM Best Student Paper, 2020 AAAI ICWSM Best Paper), and the 2016 Eugene L. Grant Undergraduate Teaching Award from the Department of Management Science & Engineering.