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An Exploratory Study of COVID-19 Misinformation on Twitter

arXiv 2005.05710 ↗First posted 12 May 2020cs.SI cs.CYcited by 33

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Abstract (arXiv)

During the COVID-19 pandemic, social media has become a home ground for misinformation. To tackle this infodemic, scientific oversight, as well as a better understanding by practitioners in crisis management, is needed. We have conducted an exploratory study into the propagation, authors and content of misinformation on Twitter around the topic of COVID-19 in order to gain early insights. We have collected all tweets mentioned in the verdicts of fact-checked claims related to COVID-19 by over 92 professional fact-checking organisations between January and mid-July 2020 and share this corpus with the community. This resulted in 1 500 tweets relating to 1 274 false and 276 partially false claims, respectively. Exploratory analysis of author accounts revealed that the verified twitter handle(including Organisation/celebrity) are also involved in either creating (new tweets) or spreading (retweet) the misinformation. Additionally, we found that false claims propagate faster than partially false claims. Compare to a background corpus of COVID-19 tweets, tweets with misinformation are more often concerned with discrediting other information on social media. Authors use less tentative language and appear to be more driven by concerns of potential harm to others. Our results enable us to suggest gaps in the current scientific coverage of the topic as well as propose actions for authorities and social media users to counter misinformation.

Key facts. Cited by 33 papers in the map (data through 7 Oct 2026). First posted on arXiv 12 May 2020.

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Citing papers per year: 2021: 13; 2022: 12; 2023: 5; 2024: 0; 2025: 3.

33 papers.

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  1. 2023Knowledge Editing for Large Language Models: A Surveycited by 189
  2. 2021Overview of the HASOC track at FIRE 2020: Hate Speech and Offensive Content Identification in Indo-European Languagescited by 38
  3. 2022CoVERT: A Corpus of Fact-checked Biomedical COVID-19 Tweetscited by 29
  4. 2021Overview of the CLEF--2021 CheckThat! Lab on Detecting Check-Worthy Claims, Previously Fact-Checked Claims, and Fake Newscited by 23
  5. 2022A Holistic Framework for Analyzing the COVID-19 Vaccine Debatecited by 18
  6. 2021Overview of the HASOC Subtrack at FIRE 2021: Hate Speech and Offensive Content Identification in English and Indo-Aryan Languagescited by 17
  7. 2022Matching Tweets With Applicable Fact-Checks Across Languagescited by 15
  8. 2022Human-in-the-loop Evaluation for Early Misinformation Detection: A Case Study of COVID-19 Treatmentscited by 14
  9. 2022METS-CoV: A Dataset of Medical Entity and Targeted Sentiment on COVID-19 Related Tweetscited by 12
  10. 2021Model Generalization on COVID-19 Fake News Detectioncited by 11
  11. 2021Dataset of Fake News Detection and Fact Verification: A Surveycited by 11
  12. 2025Beyond the Crowd: LLM-Augmented Community Notes for Governing Health Misinformationcited by 10
21 more
  1. 2021Mitigation of Diachronic Bias in Fake News Detection Datasetcited by 5
  2. 2022Natural Language Inference with Self-Attention for Veracity Assessment of Pandemic Claimscited by 5
  3. 2022Harnessing Abstractive Summarization for Fact-Checked Claim Detectioncited by 5
  4. 2022Applying Automatic Text Summarization for Fake News Detectioncited by 4
  5. 2021A repeated-measures study on emotional responses after a year in the pandemiccited by 3
  6. 2021NoFake at CheckThat! 2021: Fake News Detection Using BERTcited by 3
  7. 2022ArCovidVac: Analyzing Arabic Tweets About COVID-19 Vaccinationcited by 3
  8. 2021Tiplines to Combat Misinformation on Encrypted Platforms: A Case Study of the 2019 Indian Election on WhatsAppcited by 2
  9. 2022QCRI's COVID-19 Disinformation Detector: A System to Fight the COVID-19 Infodemic in Social Mediacited by 2
  10. 2021Eating Garlic Prevents COVID-19 Infection: Detecting Misinformation on the Arabic Content of Twittercited by 1
  11. 2021The Evolution of Rumors on a Closed Platform during COVID-19cited by 1
  12. 2021Checkovid: A COVID-19 misinformation detection system on Twitter using network and content mining perspectivescited by 1
  13. 2021COVID-19 Vaccine and Social Media: Exploring Emotions and Discussions on Twittercited by 1
  14. 2023X-CapsNet For Fake News Detectioncited by 1
  15. 2022A Weakly-Supervised Iterative Graph-Based Approach to Retrieve COVID-19 Misinformation Topicscited by 0
  16. 2022When Infodemic Meets Epidemic: a Systematic Literature Reviewcited by 0
  17. 2023BotArtist: Generic approach for bot detection in Twitter via semi-automatic machine learning pipelinecited by 0
  18. 2023Machine Learning Technique Based Fake News Detectioncited by 0
  19. 2023Understanding the Humans Behind Online Misinformation: An Observational Study Through the Lens of the COVID-19 Pandemiccited by 0
  20. 2025Multimodal Misinformation Detection Using Early Fusion of Linguistic, Visual, and Social Featurescited by 0
  21. 2025WhatsApp Tiplines and Multilingual Claims in the 2021 Indian Assembly Electionscited by 0