  Sharing misinformation threatens societies as misleading news shapes the risk
perception of individuals. We witnessed this during the COVID-19 pandemic,
where misinformation undermined the effectiveness of stay-at-home orders,
posing an additional obstacle in the fight against the virus. In this research,
we study misinformation spreading, reanalyzing behavioral data on online
sharing, and analyzing decision-making mechanisms using the Drift Diffusion
Model (DDM). We find that subjects display an increased instinctive inclination
towards sharing misleading news, but rational thinking significantly curbs this
reaction, especially for more cautious and older individuals. Using an
agent-based model, we expand this individual knowledge to a social network
where individuals are exposed to misinformation through friends and share (or
not) content with probabilities driven by DDM. The natural shape of the Twitter
network provides a fertile ground for any news to rapidly become viral, yet we
found that limiting users' followers proves to be an appropriate and feasible
containment strategy.
