Modeling the Spreading of Fake News Through the Interactions Between Human Heuristics and Recommender Systems

In many cases, the pieces of information at our disposal (messages) come from a recommender source, which can be either an official news system, a large language model or simply a social network. Often, also, these messages are build so as to promote their active spreading, which, on the other hand, has a positive effect on one’s own popularity. However, the content of the message can be false, giving origin to a phenomenon analogous to the spreading of a disease. In principle, there is always the possibility of checking the correctness of the message by “investing” some time, so we can say that this checking has a cost. We develop a simple model based on the mechanism of “risk perception” (propensity to check the falseness of a message) and mutual trustability (affinity), based on the average number of fake messages received and checked. On the other side, the probability of emitting a fake message is inversely proportional to one’s risk perception and the affinity among agents is also exploited by the recommender system. We aim at investigating this process with the goal of deriving methods for identifying the penetration level of fake news from behavioral patterns of users. This model represents an integration of cognitive psychology with computational agent-based modeling.