A new phenomenon is the spread and acceptance of “fake news” 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. This paper takes it to a new level: 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. We used two previously collected large data sets with a total of 3309 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. A hybrid model that combines those models outperformed the prediction of all individual models pointing to an adaptive tool-box.