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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| Vydané v: | Scientific reports Ročník 10; číslo 1; s. 16598 |
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| Hlavní autori: | , , , , , , , , |
| Médium: | Journal Article |
| Jazyk: | English |
| Vydavateľské údaje: |
London
Nature Publishing Group UK
06.10.2020
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| Predmet: | |
| ISSN: | 2045-2322, 2045-2322 |
| On-line prístup: | Získať plný text |
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| 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
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. |
|---|---|
| AbstractList | 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. 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. 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.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. 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 $$R_0$$ R0 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. 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 $$R_0$$ 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. |
| ArticleNumber | 16598 |
| Author | Schmidt, Ana Lucia Cinelli, Matteo Zollo, Fabiana Quattrociocchi, Walter Valensise, Carlo Michele Zola, Paola Galeazzi, Alessandro Brugnoli, Emanuele Scala, Antonio |
| Author_xml | – sequence: 1 givenname: Matteo surname: Cinelli fullname: Cinelli, Matteo organization: CNR-ISC, Università Ca’ Foscari di Venezia – sequence: 2 givenname: Walter surname: Quattrociocchi fullname: Quattrociocchi, Walter email: w.quattrociocchi@unive.it organization: CNR-ISC, Università Ca’ Foscari di Venezia, Big Data in Health Society – sequence: 3 givenname: Alessandro surname: Galeazzi fullname: Galeazzi, Alessandro organization: Università di Brescia – sequence: 4 givenname: Carlo Michele surname: Valensise fullname: Valensise, Carlo Michele organization: Politecnico di Milano – sequence: 5 givenname: Emanuele surname: Brugnoli fullname: Brugnoli, Emanuele organization: CNR-ISC – sequence: 6 givenname: Ana Lucia surname: Schmidt fullname: Schmidt, Ana Lucia organization: Università Ca’ Foscari di Venezia – sequence: 7 givenname: Paola surname: Zola fullname: Zola, Paola organization: CNR-IIT – sequence: 8 givenname: Fabiana surname: Zollo fullname: Zollo, Fabiana organization: CNR-ISC, Università Ca’ Foscari di Venezia, Center for the Humanities and Social Change – sequence: 9 givenname: Antonio surname: Scala fullname: Scala, Antonio organization: CNR-ISC, Big Data in Health Society |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/33024152$$D View this record in MEDLINE/PubMed |
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| Title | The COVID-19 social media infodemic |
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