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.

翻译：虚假信息易感性描述了个人对未经证实说法相信的程度，这一过程隐藏于人类心理活动中且难以直接观测。现有易感性研究严重依赖自我报告信念，导致任何基于易感性的下游应用难以规模化。为解决上述局限，本文提出了一种计算模型，可通过用户活动推断其易感性水平。由于用户易感性是其转发行为的关键指标，我们利用可观测分享行为的监督信号来推断潜在易感性倾向。评估结果表明，该模型对用户易感性水平的相对判断与人类评估高度一致。基于此大规模易感性标注，我们进一步系统分析了不同社会因素与易感性的关联，发现政治倾向和心理因素与易感性存在不同程度的关联。