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The COVID-19 social media infodemic

Scientific Reports · 2020 · Vol. 10(1) · pp. 16598–16598
Matteo CinelliWalter QuattrociocchiAlessandro GaleazziCarlo Michele ValensiseEmanuele BrugnoliAna Lucia SchmidtPaola ZolaFabiana ZolloAntonio Scala

Abstract

We address the diffusion of information about the COVID-19 with a massive data analysis on Twitter, Instagram, YouTube, Reddit and Gab. We analyze engagement and interest in the COVID-19 topic and provide a differential assessment on the evolution of the discourse on a global scale for each platform and their users. We fit information spreading with epidemic models characterizing the basic reproduction number [Formula: see text] for each social media platform. Moreover, we identify information spreading from questionable sources, finding different volumes of misinformation in each platform. However, information from both reliable and questionable sources do not present different spreading patterns. Finally, we provide platform-dependent numerical estimates of rumors' amplification.

Misinformation and Its ImpactsComplex Network Analysis TechniquesOpinion Dynamics and Social InfluenceMisinformationSocial mediaScale (ratio)Topic modelReproductionHomophily

MeSH terms

BetacoronavirusData AnalysisCOVID-19SARS-CoV-2HumansPneumonia, ViralSocial BehaviorLinear ModelsNeural Networks, ComputerCoronavirus InfectionsInformation DisseminationBasic Reproduction NumberPandemicsSocial Media
Citations
1,549
FWCI
389.57
field-weighted impact
References
35
Percentile
100%
vs. same field & year
Citations per year
References
Learning representations by back-propagating errors
Nature · 1986 · 30,045 citations
The spread of true and false news online
Science · 2018 · 8,017 citations
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