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Susceptibility to misinformation describes the extent to believe unverifiableclaims, which is hidden in people's mental process and infeasible to observe.Existing susceptibility studies heavily rely on the self-reported beliefs,making any downstream applications on susceptability hard to scale. To addressthese limitations, in this work, we propose a computational model to inferusers' susceptibility levels given their activities. Since user'ssusceptibility is a key indicator for their reposting behavior, we utilize thesupervision from the observable sharing behavior to infer the underlyingsusceptibility tendency. The evaluation shows that our model yields estimationsthat are highly aligned with human judgment on users' susceptibility levelcomparisons. Building upon such large-scale susceptibility labeling, we furtherconduct a comprehensive analysis of how different social factors relate tosusceptibility. We find that political leanings and psychological factors areassociated with susceptibility in varying degrees.

From Scroll to Misbelief: Modeling the Unobservable Susceptibility to Misinformation on Social Media