Quantifying the impact of persuasiveness, cautiousness and prior beliefs in (mis)information sharing on online social networks using Drift Diffusion Models (2401.17846v2)

Abstract: Misleading newsletters can shape individuals' perceptions, and pose a threat to societies; as we witnessed by lowering the severity of follow-up stay-at-home orders and burdening a significant challenge to the fight against COVID-19. In this research, we study (mis)information 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. On top of network structures with similar characteristics as X, Mastodon, and Facebook, we use an agent-based model to expand this individual knowledge to a large scale where individuals are exposed to (mis)information through friends and share (or not) content with probabilities driven by DDM. We found that the natural shape of these social online networks provides a fertile ground for any news to rapidly become viral. Yet we have found that, for the case of X, limiting the number of followers of the most connected users proves to be an appropriate and feasible containment strategy.