The spreading of misinformation online

Abstract

Significance The wide availability of user-provided content in online social media facilitates the aggregation of people around common interests, worldviews, and narratives. However, the World Wide Web is a fruitful environment for the massive diffusion of unverified rumors. In this work, using a massive quantitative analysis of Facebook, we show that information related to distinct narratives––conspiracy theories and scientific news––generates homogeneous and polarized communities (i.e., echo chambers) having similar information consumption patterns. Then, we derive a data-driven percolation model of rumor spreading that demonstrates that homogeneity and polarization are the main determinants for predicting cascades’ size.

Keywords

MisinformationRumorSocial mediaNarrativeComputer scienceInformation cascadeInternet privacyMedia consumptionData scienceWorld Wide WebAdvertisingPsychologyPolitical scienceComputer securitySocial psychologyPublic relationsBusiness

MeSH Terms

CommunicationComputer SimulationHumansModelsTheoreticalScienceSocial Media

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Publication Info

Year: 2016
Type: article
Volume: 113
Issue: 3
Pages: 554-559
Citations: 2185
Access: Closed

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APA Style

                            
                                
                                    Michela Del Vicario, 
                                
                                    Alessandro Bessi, 
                                
                                    Fabiana Zollo
                                
                                et al.
                            
                            (2016). 
                            The spreading of misinformation online. 
                            Proceedings of the National Academy of Sciences
                            , 113
                            (3)
                            , 554-559.
                            https://doi.org/10.1073/pnas.1517441113
                        

Identifiers

DOI: 10.1073/pnas.1517441113
PMID: 26729863
PMCID: PMC4725489

Data Quality

Data completeness: 86%