The COVID-19 social media infodemic

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...

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Veröffentlicht in:Scientific reports Jg. 10; H. 1; S. 16598
Hauptverfasser: Cinelli, Matteo, Quattrociocchi, Walter, Galeazzi, Alessandro, Valensise, Carlo Michele, Brugnoli, Emanuele, Schmidt, Ana Lucia, Zola, Paola, Zollo, Fabiana, Scala, Antonio
Format: Journal Article
Sprache:Englisch
Veröffentlicht: London Nature Publishing Group UK 06.10.2020
Schlagworte:
639/705/117
639/766/259
692/308/174
Basic Reproduction Number
Betacoronavirus
Coronavirus Infections - epidemiology
Coronavirus Infections - virology
COVID-19
Data Analysis
Humanities and Social Sciences
Humans
Information Dissemination
Linear Models
multidisciplinary
Neural Networks, Computer
Pandemics
Pneumonia, Viral - epidemiology
Pneumonia, Viral - virology
SARS-CoV-2
Science
Science (multidisciplinary)
Social Behavior
Social Media
ISSN:2045-2322, 2045-2322
Online-Zugang:Volltext
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Beschreibung
Zusammenfassung: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 R 0 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.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-020-73510-5