Bridging-based Fact-checking Moderates the Diffusion of False Information on Social Media

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 online social platforms, including the structural differences between their diffusion patterns. However, much less is known about how platform interventions on false content alter the engagement with and diffusion of such content. In this work, we estimate the causal effects of Community Notes, a novel fact-checking feature adopted by X (formerly Twitter) to solicit and vet crowd-sourced fact-checking notes for false content. We gather detailed time series data for 40,074 posts for which notes have been proposed and use synthetic control methods to estimate a range of counterfactual outcomes. We find that attaching fact-checking notes significantly reduces the engagement with and diffusion of false content. We estimate that, on average, the notes resulted in reductions of 45.7% in reposts, 43.5% in likes, 22.9% in replies, and 14.0% in views after being attached. Over the posts’ entire lifespans, these reductions amount to 11.4% fewer reposts, 13.0% fewer likes, 7.3% fewer replies, and 5.7% fewer views on average. In reducing reposts, we observe that diffusion cascades for fact-checked content are less deep, but not less broad, than synthetic control estimates for non-fact-checked content with similar reach. This structural difference contrasts notably with differences between false vs. true content diffusion itself, where false information diffuses farther, but with structural patterns that are otherwise indistinguishable from those of true information, conditional on reach. Joint work with Isaac Slaughter, Axel Peytevin, and Martin Saveski.

Biography:

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 postdoctoral 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.