Our latest “Fact-checking strategies to limit urban legends spreading in a segregated society” is finally out and accessible as open access. Published by Applied Network Science, 4, Article number: 116 (2019), authors Marcella Tambuscio and Giancarlo Ruffo.
We propose a framework to study the spreading of urban legends, i.e., false stories that become persistent in a local popular culture, where social groups are naturally segregated by virtue of many (both mutable and immutable) attributes. The goal of this work is identifying and testing new strategies to restrain the dissemination of false information, focusing on the role of network polarization. Following the traditional approach in the study of information diffusion, we consider an epidemic network-based model where the agents can be ‘infected’ after being exposed to the urban legend or to its debunking depending on the belief of their neighborhood. Simulating the spreading process on several networks showing different kind of segregation, we perform a what-if analysis to compare strategies and to understand where it is better to locate eternal fact-checkers, nodes that maintain their position as debunkers of the given urban legend. Our results suggest that very few of these strategies have a chance to succeed. This apparently negative outcomes turns out to be somehow surprising taking into account that we ran our simulations under a highly pessimistic assumption, such that the ‘believers’, i.e., agents that accepted as true the urban legend after they have been exposed to it, will not change their belief no matter of how much external or internal additional informational sources they access to. This has implications on policies that are supposed to decide which strategy to apply to stop misinformation from spreading in real world networks.