Susceptibility to misinformation describes the degree of belief in unverifiable claims, a latent aspect of individuals' mental processes that is not observable. Existing susceptibility studies heavily rely on self-reported beliefs, which can be subject to bias, expensive to collect, and challenging to scale for downstream applications. To address these limitations, in this work, we propose a computational approach to model users' latent susceptibility levels. As shown in previous research, susceptibility is influenced by various factors (e.g., demographic factors, political ideology), and directly influences people's reposting behavior on social media. To represent the underlying mental process, our susceptibility modeling incorporates these factors as inputs, guided by the supervision of people's sharing behavior. Using COVID-19 as a testbed domain, our experiments demonstrate a significant alignment between the susceptibility scores estimated by our computational modeling and human judgments, confirming the effectiveness of this latent modeling approach. Furthermore, we apply our model to annotate susceptibility scores on a large-scale dataset and analyze the relationships between susceptibility with various factors. Our analysis reveals that political leanings and psychological factors exhibit varying degrees of association with susceptibility to COVID-19 misinformation.

翻译：对错误信息的易感性描述了个体对不可验证主张的相信程度，这是个体心理过程中不可直接观测的潜在维度。现有易感性研究主要依赖自我报告信念，这种方法可能存在偏差、收集成本高昂，且难以扩展到下游应用场景。为解决这些局限，本研究提出一种计算方法来建模用户的潜在易感性水平。先前研究表明，易感性受多种因素（如人口统计学因素、政治意识形态）影响，并直接影响人们在社交媒体上的转发行为。为表征这一潜在心理过程，我们的易感性建模将这些因素作为输入，并以人们的分享行为作为监督信号进行引导。以COVID-19为测试领域，实验表明，通过计算模型估算的易感性评分与人工判断之间存在显著一致性，验证了这种潜在建模方法的有效性。此外，我们将模型应用于大规模数据集以标注易感性评分，并分析了易感性与多种因素之间的关系。分析结果显示，政治倾向和心理因素与COVID-19错误信息易感性之间存在不同程度的关联。