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

arXiv 2304.04811 ↗First posted 10 Apr 2023cs.CLcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 13 Oct 2026). Builds on 15 of its 53 references that are in the map. First posted on arXiv 10 Apr 2023.

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15 of 53 references.

References · 15

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,304
  2. 2020COVID-Twitter-BERT: A Natural Language Processing Model to Analyse COVID-19 Content on Twittercited by 96
  3. 2020CoAID: COVID-19 Healthcare Misinformation Datasetcited by 83
  4. 2020The COVID-19 Social Media Infodemiccited by 64
  5. 2020Characterizing COVID-19 Misinformation Communities Using a Novel Twitter Datasetcited by 32
  6. 2020The Role of the Crowd in Countering Misinformation: A Case Study of the COVID-19 Infodemiccited by 24
  7. 2023VaxxHesitancy: A Dataset for Studying Hesitancy towards COVID-19 Vaccination on Twittercited by 14
  8. 2020A first look at COVID-19 information and misinformation sharing on Twittercited by 13
  9. 2020No Rumours Please! A Multi-Indic-Lingual Approach for COVID Fake-Tweet Detectioncited by 13
  10. 2021Categorising Fine-to-Coarse Grained Misinformation: An Empirical Study of COVID-19 Infodemiccited by 9
  11. 2020Mega-COV: A Billion-Scale Dataset of 100+ Languages for COVID-19cited by 5
  12. 2020Drink Bleach or Do What Now? Covid-HeRA: A Study of Risk-Informed Health Decision Making in the Presence of COVID-19 Misinformationcited by 5
3 more
  1. 2022Identifying and Characterizing Active Citizens who Refute Misinformation in Social Mediacited by 5
  2. 2021CML-COVID: A Large-Scale COVID-19 Twitter Dataset with Latent Topics, Sentiment and Location Informationcited by 1
  3. 2021The State of Infodemic on Twittercited by 1

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