PlumX Metrics
Embed PlumX Metrics

The COVID-19 Infodemic: Twitter versus Facebook

Big Data and Society, ISSN: 2053-9517, Vol: 8, Issue: 1
2021
  • 123
    Citations
  • 0
    Usage
  • 247
    Captures
  • 38
    Mentions
  • 9
    Social Media
Metric Options:   Counts1 Year3 Year

Metrics Details

  • Citations
    123
    • Citation Indexes
      122
    • Policy Citations
      1
      • Policy Citation
        1
  • Captures
    247
  • Mentions
    38
    • News Mentions
      35
      • News
        35
    • Blog Mentions
      3
      • Blog
        3
  • Social Media
    9
    • Shares, Likes & Comments
      9
      • Facebook
        9

Most Recent News

Disinformation Echo-Chambers on Facebook: Funding and References

:::info This paper is available on arxiv under CC BY-SA 4.0 DEED license. Authors: (1) Mathias-Felipe de-Lima-Santos, Faculty of Humanities, University of Amsterdam, Institute of

Article Description

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.

Bibliographic Details

Kai Cheng Yang; Francesco Pierri; Pik Mai Hui; David Axelrod; Christopher Torres-Lugo; John Bryden; Filippo Menczer

SAGE Publications

Computer Science; Social Sciences; Decision Sciences

Provide Feedback

Have ideas for a new metric? Would you like to see something else here?Let us know