Decoding Susceptibility: Modeling Misbelief to Misinformation Through a Computational Approach

TL;DR

This paper introduces a computational model to estimate individuals' susceptibility to misinformation based on social media sharing behavior, addressing biases in self-reported data and enabling large-scale analysis.

Contribution

It presents a novel latent susceptibility modeling approach guided by sharing behavior, validated with COVID-19 data, and analyzes factors influencing misinformation susceptibility.

Findings

Model scores align well with human judgments.

Political and psychological factors correlate with susceptibility.

Large-scale susceptibility annotation is feasible.

Abstract

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.