Decoding Susceptibility: Modeling Misbelief to Misinformation Through a Computational Approach (2024)

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 efficiently model users’ latent susceptibility levels. As shown in previous work, 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, 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 other psychological factors exhibit varying degrees of association with susceptibility to COVID-19 misinformation, and shows that susceptibility is unevenly distributed across different professional and geographical backgrounds.

## introduction false claims spread on social media platforms, such as conspiracy theories, fake news, and unreliable health information. they mislead people's judgment, promote societal polarization, and exacerbate distrust in government @xcite . the harm is especially significant in various contentious events, including elections, religious persecution, and the global response to the covid-19 pandemic @xcite . many works have investigated the observable behavior of misinformation propagation such as where the information propagates @xcite , how people share it @xcite , and what people discuss about it @xcite . however, it is still crucial but challenging to understand the unobservable mental and cognitive processes of how individuals believe misinformation @xcite . individual susceptibility (i.e., the likelihood of believing and being influenced by misinformation) plays a pivotal role in this context. if one is more susceptible to misinformation, they are not only more likely to share but also more prone to being misled by them @xcite . previous works have investigated the psychological, demographic, and other factors that may contribute to the high susceptibility of an individual @xcite . however, these studies heavily rely on self-reported belief towards false claims collected from questionnaire-based participant surveys @xcite , which presents several limitations. for instance, different participants might interpret belief levels differently. moreover, the data collection is labor-intensive, thereby limiting the scale of downstream research on the size, scope, and diversity of the target population @xcite . people's mental processes, which are unobservable and influenced by various factors, directly affect several externalized behaviors, such as reposting on social media @xcite @xcite . building on these prior works, we propose a computational method to efficiently model individuals' unobservable susceptibility levels only based on their observable social media posting and sharing behaviors. we represent users based on their historical posts and perform multi-task learning to simultaneously learn to classify whether a user would share a post, as well as to rank susceptibility scores among similar and dissimilar users when the same content is seen. this computational modeling method unlocks the scales of misinformationrelated studies and provides a novel perspective to reveal users' belief patterns. in this paper, we focus our experiments on covid-19 misinformation, and our evaluations demonstrate that the estimations from our model are highly aligned with human judgment when assessed through a susceptibility comparison task. the correlation study between estimated and human-annotated susceptibility verifies the effectiveness of the indirect susceptibility modeling method. to further illustrate the significance of our work, we employ our model to annotate susceptibility levels on a large-scale dataset. building upon this extensive susceptibility labeling, we then conduct a set analysis to examine how various factors relate to susceptibility. our analysis reveals that psychological factors, professional fields, and political leanings are associated with susceptibility to varying degrees. notably, this large-scale analysis enabled by our computational susceptibility modeling corroborates the findings of previous studies based on self-reported beliefs, e.g. confirming that stronger analytical thinking is an indicator of lower susceptibility. moreover, the results of our analysis show the potential to extend findings in the existing literature. for example, we demonstrate that the distribution of covid-19 misinformation susceptibility in the u.s. exhibits a certain degree of correlation with political leanings. ## computational susceptibility modeling misinformation is characterized as information that is false, inaccurate, or misleading, which could be created deliberately or accidentally @xcite . the susceptibility to misinformation represents the belief in misinformation and related constructs, including discernment between true and false claims and the extent to which exposure to misinformation misleads subsequent decisions @xcite . previous research on susceptibility and misinformation mainly relied on self-reported beliefs collected using surveys or questionnaires -they suffered from problems like being subject to bias, expensive to collect, and challenging to reproduce and scale up. existing studies indicating that believing a piece of misinformation can influence various outward behaviors, such as sharing actions. for example, previous studies of the inattention or "classical reasoning" account contend that people are committed to sharing accurate information, but the unique context of social media disrupts their capacity to critically assess the accuracy of news @xcite . these studies suggest that people are more likely to share things they genuinely believe @xcite . inspired by this observation, we propose to model user's unobservable susceptibility only based on their historical posting and sharing behaviors, which are the most available and the easiest collectable data from social media ( §3.1) as shown in fig. 1 . therefore, our proposed framework can efficiently infer users' susceptibility levels to misinformation on a large scale, demonstrating the potential to expand the scope of previous misinformation-related research. furthermore, because social media users utilize posts to express their personal and inner thoughts, they reveal information about their characteristics through their posts. therefore, our proposed susceptibility modeling can incorporate users' informative hidden factors, such as personality traits, analytical thinking, and emotion, to infer a user's susceptibility to misinformation. these additional pieces of information are otherwise very difficult to directly collect on social media. ## dataset and experiment setup we have chosen twitter as our data source because it hosts a diverse collection of users and allows for free-text personal and emotional expression. furthermore, twitter provides crucial metadata, including timestamps and location data, which are useful for our subsequent analysis. ## evaluation we demonstrate the effectiveness of our susceptibility modeling by directly comparing our estimations with human judgment ( §5.1) and indirectly evaluating for assessing sharing behavior ( §5.2, §5.3). ## analysis to further illustrate the significance of our work for the computational social science community in susceptibility and misinformation research, we conducted a large-scale analysis on our collected large twitter datasets and analyzed the correlation between user's susceptibility and their psychological factors ( §6.1), professional backgrounds ( §6.2), and geographical distribution ( §6.2). our findings demonstrate that the large-scale analysis enabled by our proposed efficient susceptibility modeling not only corroborates the results of previous questionnaire-based studies, but also shows the potential of further extending the scope of research on susceptibility and misinformation. ## conclusion in this work, we propose a computational approach to efficiently model people's latent susceptibility to misinformation. while previous research on susceptibility is heavily relied on self-reported beliefs collected from questionnaire-based surveys, our model trained in a multi-task manner can estimate user's susceptibility levels only based on their posting and sharing behaviors on social media. when compared with human judgment, our model shows highly aligned predictions on a susceptibility comparison evaluation task. to demonstrate the potential of our proposed compu-tational modeling in extending the scope of previous misinformation-related studies, we leverage the susceptibility scores estimated by our model to analyze factors that influence susceptibility to covid-19 misinformation. our analysis considers a diverse population from various professional and geographical backgrounds, and the results obtained through our computational modeling not only align with but also support and extend the findings from previous survey-based social science studies.