From Scroll to Misbelief: Modeling the Unobservable Susceptibility to Misinformation on Social Media.
CoRR(2023)
摘要
Susceptibility to misinformation describes the extent to believe unverifiable
claims, 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 address
these limitations, in this work, we propose a computational model to infer
users' susceptibility levels given their activities. Since user's
susceptibility is a key indicator for their reposting behavior, we utilize the
supervision from the observable sharing behavior to infer the underlying
susceptibility tendency. The evaluation shows that our model yields estimations
that are highly aligned with human judgment on users' susceptibility level
comparisons. Building upon such large-scale susceptibility labeling, we further
conduct a comprehensive analysis of how different social factors relate to
susceptibility. We find that political leanings and psychological factors are
associated with susceptibility in varying degrees.
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