The COVID-19 Social Media Infodemic

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 numbers $R_0$ for each social media platform. Moreover, we characterize 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.

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Cited by in corpus (37)

• Why do People Share Misinformation during the COVID-19 Pandemic?
• Blockchain and AI-based Solutions to Combat Coronavirus (COVID-19)-like Epidemics: A Survey
• Shifting Polarization and Twitter News Influencers between two U.S. Presidential Elections
• Misinformation, Believability, and Vaccine Acceptance Over 40 Countries: Takeaways From the Initial Phase of The COVID-19 Infodemic
• On a Network SIS Epidemic Model with Cooperative and Antagonistic Opinion Dynamics
• One Year of COVID-19 Vaccine Misinformation on Twitter: Longitudinal Study
• How the world's collective attention is being paid to a pandemic: COVID-19 related n-gram time series for 24 languages on Twitter
• Sustained Online Amplification of COVID-19 Elites in the United States
• Classification Aware Neural Topic Model and its Application on a New COVID-19 Disinformation Corpus
• Flow of online misinformation during the peak of the COVID-19 pandemic in Italy
• Using Twitter Data to Understand Public Perceptions of Approved versus Off-label Use for COVID-19-related Medications
• Interplay between risk perception, behaviour, and COVID-19 spread
• Causal Modeling of Twitter Activity During COVID-19
• Drivers of social influence in the Twitter migration to Mastodon
• Dark Web Marketplaces and COVID-19: before the vaccine
• The simulation of judgment in LLMs
• Firms' Challenges and Social Responsibilities during Covid-19: a Twitter Analysis
• Writing about COVID-19 vaccines: Emotional profiling unravels how mainstream and alternative press framed AstraZeneca, Pfizer and vaccination campaigns
• How does Twitter account moderation work? Dynamics of account creation and suspension on Twitter during major geopolitical events
• Large-scale, Language-agnostic Discourse Classification of Tweets During COVID-19
• Sustainable Development Goals as unifying narratives in large UK firms' Twitter discussions
• Testing the Generalization of Neural Language Models for COVID-19 Misinformation Detection
• Bi-stability of SUDR+K model of epidemics and test kits applied to COVID-19
• Social stress drives the multi-wave dynamics of COVID-19 outbreaks
• Effects of Online Self-Disclosure on Social Feedback During the COVID-19 Pandemic
• Can the Crowd Judge Truthfulness? A Longitudinal Study on Recent Misinformation about COVID-19
• Complexity of Government response to Covid-19 pandemic: A perspective of coupled dynamics on information heterogeneity and epidemic outbreak
• On the dynamics emerging from pandemics and infodemics
• Moral decisions in the age of COVID-19: your choices really matter
• Data-informed modeling of the formation, persistence, and evolution of social norms and conventions
• Predicting, Evaluating, and Explaining Top Misinformation Spreaders via Archetypal User Behavior
• Modelling and Predicting Online Vaccination Views using Bow-tie Decomposition
• Highly engaging events reveal semantic and temporal compression in online community discourse
• Thermodynamics of Innovation: A Statistical Mechanics Framework of Social Adoption
• An evidence-accumulating drift-diffusion model of competing information spread on networks
• Italian Twitter semantic network during the Covid-19 epidemic
• Social Contagion in COVID-19 Discussions within the Belgian Reddit Community: A Statistical and Modeling Study