A Large-Scale Comparative Study of Accurate COVID-19 Information versus Misinformation

Yida Mu, Ye Jiang, Freddy Heppell, Iknoor Singh, Carolina Scarton +2 more
4/10/2023
cs.CL

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Abstract

The COVID-19 pandemic led to an infodemic where an overwhelming amount of COVID-19 related content was being disseminated at high velocity through social media. This made it challenging for citizens to differentiate between accurate and inaccurate information about COVID-19. This motivated us to carry out a comparative study of the characteristics of COVID-19 misinformation versus those of accurate COVID-19 information through a large-scale computational analysis of over 242 million tweets. The study makes comparisons alongside four key aspects: 1) the distribution of topics, 2) the live status of tweets, 3) language analysis and 4) the spreading power over time. An added contribution of this study is the creation of a COVID-19 misinformation classification dataset. Finally, we demonstrate that this new dataset helps improve misinformation classification by more than 9\% based on average F1 measure.

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Cite this paper

@article{mu2023largescale,
  title  = {A Large-Scale Comparative Study of Accurate COVID-19 Information versus Misinformation},
  author = {Yida Mu and Ye Jiang and Freddy Heppell and Iknoor Singh and Carolina Scarton and Kalina Bontcheva and Xingyi Song},
  year   = {2023},
  eprint = {2304.04811},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2304.04811v2}
}

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