The COVID-19 Infodemic: Twitter versus Facebook

The COVID-19 Infodemic: Twitter versus Facebook
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DOI:
10.1177/20539517211013861
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发表时间:
2020-12
期刊:
影响因子:
8.5
通讯作者:
Kai-Cheng Yang;Francesco Pierri;Pik-Mai Hui;David Axelrod;Christopher Torres-Lugo;J. Bryden;F. Menczer
Kai-Cheng Yang;Francesco Pierri;Pik-Mai Hui;David Axelrod;Christopher Torres-Lugo;J. Bryden;F. Menczer
中科院分区:
法学1区
文献类型:
--
作者:
Kai-Cheng Yang;Francesco Pierri;Pik-Mai Hui;David Axelrod;Christopher Torres-Lugo;J. Bryden;F. Menczer

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新型冠状病毒的全球传播受到相关错误信息传播的影响-所谓的COVID-19 Infodemic-通过抵抗缓解努力使人口更容易受到疾病的影响。在这里,我们分析了两个主要社交媒体平台Twitter和Facebook上关于大流行病的低可信度内容的链接的流行和传播情况。我们在流行的来源,扩散模式,影响者,协调和自动化方面描述了跨平台的相似性和差异。对比两个平台,我们发现流行的低可信度来源和可疑视频的流行程度存在差异。少数账户和页面对每个平台都有很大的影响力。这些错误信息的“超级传播者”通常与低可信度的来源相关联,并且往往会被平台验证。在这两个平台上,有证据表明信息流行内容的协调共享。这种操纵的明显性质表明,除了平台内的缓解战略外,还需要社会层面的解决方案。然而,我们强调不一致的数据访问政策对我们研究信息生态系统有害操纵的能力造成的限制。
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.