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"COVID-19 was a FIFA conspiracy #curropt": An Investigation into the Viral Spread of COVID-19 Misinformation

arXiv 2207.01483 ↗First posted 12 Jun 2022cs.CY cs.CL cs.SIcited by 0

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Abstract (arXiv)

The outbreak of the infectious and fatal disease COVID-19 has revealed that pandemics assail public health in two waves: first, from the contagion itself and second, from plagues of suspicion and stigma. Now, we have in our hands and on our phones an outbreak of moral controversy. Modern dependency on social medias has not only facilitated access to the locations of vaccine clinics and testing sites but also-and more frequently-to the convoluted explanations of how "COVID-19 was a FIFA conspiracy"[1]. The MIT Media Lab finds that false news "diffuses significantly farther, faster, deeper, and more broadly than truth, in all categories of information, and by an order of magnitude"[2]. The question is, how does the spread of misinformation interact with a physical epidemic disease? In this paper, we estimate the extent to which misinformation has influenced the course of the COVID-19 pandemic using natural language processing models and provide a strategy to combat social media posts that are likely to cause widespread harm.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 5 of its 30 references that are in the map. First posted on arXiv 12 Jun 2022.

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5 of 30 references.

References · 5

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  2. 2020COVID-Twitter-BERT: A Natural Language Processing Model to Analyse COVID-19 Content on Twittercited by 96
  3. 2018Towards Automated Factchecking: Developing an Annotation Schema and Benchmark for Consistent Automated Claim Detectioncited by 55
  4. 2020Characterizing COVID-19 Misinformation Communities Using a Novel Twitter Datasetcited by 32
  5. 2020Gradient-Based Adversarial Training on Transformer Networks for Detecting Check-Worthy Factual Claimscited by 5

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