When Does an Individual Accept Misinformation? An Extended Investigation Through Cognitive Modeling

A new phenomenon is the spread and acceptance of misinformation and disinformation on an individual user level, facilitated by social media such as Twitter. So far, state-of-the-art socio-psychological theories and cognitive models focus on explaining how the accuracy of fake news is judged on average, with little consideration of the individual. In this paper, a breadth of core models are comparatively assessed on their predictive accuracy for the individual decision maker, i.e., how well can models predict an individual's decision before the decision is made. To conduct this analysis, it requires the raw responses of each individual and the implementation and adaption of theories to predict the individual's response. Building on methods formerly applied on smaller and more limited datasets, we used three previously collected large datasets with a total of 3794 participants and searched for, analyzed and refined existing classical and heuristic modeling approaches. The results suggest that classical reasoning, sentiment analysis models and heuristic approaches can best predict the "Accept" or "Reject" response of a person, headed by a model put together from research by Jay Van Bavel, while other models such as an implementation of "motivated reasoning" performed worse. Further, hybrid models that combine pairs of individual models achieve a significant increase in performance, pointing to an adaptive toolbox.