The COVID-19 Infodemic: Twitter versus Facebook
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Abstract (arXiv)
The global spread of the novel coronavirus is affected by the spread of related misinformation -- the so-called COVID-19 Infodemic -- that makes populations more vulnerable to the disease through resistance to mitigation efforts. Here we analyze the prevalence and diffusion of links to low-credibility content about the pandemic across two major social media platforms, Twitter and Facebook. We characterize cross-platform similarities and differences in popular sources, diffusion patterns, influencers, coordination, and automation. Comparing the two platforms, we find divergence among the prevalence of popular low-credibility sources and suspicious videos. A minority of accounts and pages exert a strong influence on each platform. These misinformation "superspreaders" are often associated with the low-credibility sources and tend to be verified by the platforms. On both platforms, there is evidence of coordinated sharing of Infodemic content. The overt nature of this manipulation points to the need for societal-level solutions in addition to mitigation strategies within the platforms. However, we highlight limits imposed by inconsistent data-access policies on our capability to study harmful manipulations of information ecosystems.
Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). First posted on arXiv 17 Dec 2020.
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Citing papers per year: 2021: 1; 2022: 1; 2023: 3; 2024: 0; 2025: 0; 2026: 2.
7 papers.
- 2021Sparks: Inspiration for Science Writing using Language Modelscited by 80
- 2022Writing about COVID-19 vaccines: Emotional profiling unravels how mainstream and alternative press framed AstraZeneca, Pfizer and vaccination campaignscited by 3
- 2023Decoding Susceptibility: Modeling Misbelief to Misinformation Through a Computational Approachcited by 3
- 2023Leveraging Social Interactions to Detect Misinformation on Social Mediacited by 1
- 2023BotArtist: Generic approach for bot detection in Twitter via semi-automatic machine learning pipelinecited by 0
- 2026Unmasking Superspreaders: Data-Driven Approaches for Identifying and Comparing Key Influencers of Conspiracy Theories on X.comcited by 0
- 2026Predicting, Evaluating, and Explaining Top Misinformation Spreaders via Archetypal User Behaviorcited by 0
- 2026Predicting, Evaluating, and Explaining Top Misinformation Spreaders via Archetypal User Behavior17 Aug 2026
- 2026Unmasking Superspreaders: Data-Driven Approaches for Identifying and Comparing Key Influencers of Conspiracy Theories on X.com4 Feb 2026
- 2023Decoding Susceptibility: Modeling Misbelief to Misinformation Through a Computational Approach16 Nov 2023
- 2023BotArtist: Generic approach for bot detection in Twitter via semi-automatic machine learning pipeline31 May 2023
- 2023Leveraging Social Interactions to Detect Misinformation on Social Media6 Apr 2023
- 2022Writing about COVID-19 vaccines: Emotional profiling unravels how mainstream and alternative press framed AstraZeneca, Pfizer and vaccination campaigns19 Jan 2022
- 2021Sparks: Inspiration for Science Writing using Language Models14 Oct 2021