  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.
