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Demographics and topics impact on the co-spread of COVID-19 misinformation and fact-checks on Twitter

机译:人口统计和主题对Covid-19错误信息的共传播和事实检查的影响

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摘要

Correcting misconceptions and false beliefs are important for injecting reliable information about COVID-19 into public discourse, but what impact does this have on the continued proliferation of misinforming claims? Fact-checking organisations produce content with the aim of reducing misinformation spread, but our knowledge of its impact on misinformation for particular topics and demographics is limited. In this article, we explore the relation between misinformation and fact-checking spread during the COVID-19 pandemic for different topics, user demographics and attributes. We specifically follow misinformation and fact-checks emerging from December 2019 until the 4th of January 2021 on Twitter. Using a combination of spread variance analysis, impulse response modelling and causal analysis, we highlight the bidirectional, weak causation spread behaviour between misinformation and fact-checks. Although we observe that fact-checks about COVID-19 are appearing fairly quickly after misinformation is circulated, its ability to reduce overall misinformation spread appears to be limited. This is especially visible for misinformation about conspiracy theories and the causes of the virus.
机译:纠正误解和虚假信仰对于将关于Covid-19的可靠信息注入公共话语是重要的,但这对误导索赔的持续扩散有所影响?事实检查组织的内容具有减少错误信息传播的目标,但我们对其对特定主题和人口统计学的误导的影响的了解是有限的。在本文中,我们探讨了在Covid-19大流行期间的错误信息和事实检查,针对不同主题,用户人口统计数据和属性的关系。我们特别关注2019年12月的误导和事实检查,直到推特2021年1月4日。使用展差分析,脉冲响应建模和因果分析的组合,我们突出了误导性和事实检查之间的双向,弱因果传播行为。虽然我们观察到关于Covid-19的事实检查在错误信息被循环后出现相当迅速,但其降低整体错误信息传播的能力似乎有限。这对于关于阴谋理论和病毒原因的错误信息特别可见。

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