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Title: The COVID-19 Infodemic: Twitter versus Facebook
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.  more » « less
Award ID(s):
1735095
NSF-PAR ID:
10289336
Author(s) / Creator(s):
; ; ; ; ; ;
Date Published:
Journal Name:
Big Data & Society
Volume:
8
Issue:
1
ISSN:
2053-9517
Page Range / eLocation ID:
205395172110138
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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