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.

在本文中，我们提出了一个计算模型，通过用户的活动来推测其易受误导程度，并发现政治倾向和心理因素与易受误导性有不同程度的关联。

从卷轴到错误观念：对社交媒体上不可观测的误信息易感性建模