The COVID-19 social media infodemic

By Matteo Cinelli, Walter Quattrociocchi, Alessandro Galeazzi, Carlo Michele Valensise, Emanuele Brugnoli, Ana Lucía Schmidt, Paola Zola, Fabiana Zollo, Antonio Scala. Scientific Reports, 2020

1,946 citations recorded by the source corpus.

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 R0\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$R_0$$\end{document} 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.

Subfields: Sociology and Political Science, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/79ddbeff892277592aa387045692e0a738ef9edf

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Citations in the corpus

ChatGPT and the rise of large language models: the new AI-driven infodemic threat in public health (2023, background): “Even more than scientific literature, social media have been the ground of health information dissemination during the COVID-19 pandemic, with the rise of a phenomenon known as infodemic (8).”
Social media and vaccine hesitancy: new updates for the era of COVID-19 and globalized infectious diseases (2020, background): “discourse, with Twitter reporting a COVID-19 related tweet every 45 milliseconds and the hashtag #coronavirus rapidly becoming the 2 most used in 2020.(41,42) Unfortunately, false and misleading information about COVID-19, potentially dangerous treatments, and eventual vaccination continues to grow ”
Mapping the Landscape of Artificial Intelligence Applications against COVID-19 (2020, background): “In a broad-ranging study, Cinelli et al. (2020) † analyze interaction and engagement with COVID-19-related social media content.”
Growing polarization around climate change on social media (2021, background): “Historically, the dominant focus of these studies has been political polarisation in the United States and Europe [20, 23], with a recent shift towards studying the Covid-19 discussion [17, 21, 22].”
An exploratory study of COVID-19 misinformation on Twitter (2020, neutral): “This method was also employed by [15] for the propagation of information to study how infectious information on COVID-19 is on Twitter.”
Detecting East Asian Prejudice on Social Media (2020, background): “East Asian prejudice has also been linked to the spread of COVID-19 health-related misinformation [21].”
Twitter Discussions and Emotions About the COVID-19 Pandemic: Machine Learning Approach (2020, background): “Existing studies have indicated the feasibility of using LDA to identify the patterns and themes of tweets related to COVID-19 [11,22].”
Leveraging Data Science to Combat COVID-19: A Comprehensive Review (2020, background): “[148] analyse Twitter, Instagram, YouTube, Reddit and Gab data on COVID-19.”
Predicting Anti-Asian Hateful Users on Twitter during COVID-19 (2021, result): “Words related to infodemic, such as propaganda, fake, wrong, lying, and truth, are also presented, which is well aligned with a previous report (Cinelli et al., 2020).”