Realistic Aspects of Simulation Models for Fake News Epidemics over Social Networks

Quintino Francesco Lotito,Davide Zanella,Paolo Casari

doi:10.3390/fi13030076

Abstract

The pervasiveness of online social networks has reshaped the way people access information. Online social networks make it common for users to inform themselves online and share news among their peers, but also favor the spreading of both reliable and fake news alike. Because fake news may have a profound impact on the society at large, realistically simulating their spreading process helps evaluate the most effective countermeasures to adopt. It is customary to model the spreading of fake news via the same epidemic models used for common diseases; however, these models often miss concepts and dynamics that are peculiar to fake news spreading. In this paper, we fill this gap by enriching typical epidemic models for fake news spreading with network topologies and dynamics that are typical of realistic social networks. Specifically, we introduce agents with the role of influencers and bots in the model and consider the effects of dynamical network access patterns, time-varying engagement, and different degrees of trust in the sources of circulating information. These factors concur with making the simulations more realistic. Among other results, we show that influencers that share fake news help the spreading process reach nodes that would otherwise remain unaffected. Moreover, we emphasize that bots dramatically speed up the spreading process and that time-varying engagement and network access change the effectiveness of fake news spreading.

Highlights

Every agent is characterized by its own set of parameters, chosen according to statistical distributions in order to create heterogeneity; We propose a network construction algorithm that favors the emergence both of geographical user clusters and of clusters of users with common interests; this improves the degree of realism of the final simulated social network connections; we account for the presence of influencers and bots and for unequal trust among agents; We propose ways to add time dynamical effects in fake news epidemic models, by modeling the connection time of each agent to the social network and the decay of the population’s interest on a news item over time
We introduce the problem of constructing a synthetic network that resembles a real online social networks (OSNs) and state our assumptions about the time dynamics involved in our fake news epidemic model
We present the results of the fake news epidemic from a number of simulations with different starting conditions

Summary

Introduction

The pervasiveness of online social networks (OSNs) has given people unprecedented power to share information and reach a huge number of peers, both in a short amount of time and at no cost. This has shaped the way people interact and access information: currently, it has become more common to access news on social media, rather than via traditional sources [1]. A drawback of this trend is that OSNs are very fertile grounds to spread false information [2]. Users need to continuously check facts, and possibly establish reliable news access channels

Results

Discussion

Conclusion