Fact-checking strategies to limit urban legends spreading in a segregated society

Marcella Tambuscio,Giancarlo Ruffo

doi:10.1007/s41109-019-0233-1

Marcella Tambuscio, Giancarlo Ruffo

Open Access

https://doi.org/10.1007/s41109-019-0233-1

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Abstract

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.

Highlights

Our goal is to investigate new strategies to limit false news spreading, specially in presence of existing structural, geographical and/or social barriers
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 attributes
Believers and Fact Checkers can return to the Susceptible state with a fixed forgetting probability pf; Please observe that this model is a ‘pessimistic’ variation of a previous model (Tambuscio et al 2015), that follows the traditional approach of epidemic spreading (Moreno et al 2004) to understand misinformation diffusion dynamics; in the previous model, we introduced the possibility for an agent to switch from Believer to Fact Checker with a given verifying probability pv (Tambuscio et al 2015), meaning that the debunking can be spread by external factors

Summary

Introduction

Our goal is to investigate new strategies to limit false news spreading, specially in presence of existing structural, geographical and/or social barriers. Online information consumption and polarization Recently misinformation has been largely discussed (Lazer et al 2018; Vosoughi et al 2018) because it can imply serious consequences to our lives: even if in some cases fake news are intentionally disseminated to manipulate public opinion, there is a large amount of persistent rumors, or urban legends, that look as simple popular stories but are often related with social problems and leverage on fears, prejudices and emotions of people (Campion-Vincent 2017; Heath et al 2001) In this framework, digital technologies as online social networks can facilitate the spreading of misinformation, specially because they are homophily-driven, built with the intent to connect like-minded people and often exhibit the presence of echo chambers, highly segregated environments with low content diversity and high degree of repetition (Adamic and Glance 2005; Conover et al 2011; Pariser 2011; Bozdag and van den Hoven 2015). These mechanisms of algorithmic personalization have been largely debated in literature to understand if they affect the evolution of opinions (Rossi et al 2018; Bressan et al 2016) and polarize the network (Perra and Rocha 2019; Dandekar et al 2013; Geschke et al 2019), or if, they do not have a leading role in the formation of echo chambers (Möller et al 2018; Bakshy et al 2015)

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