The False COVID-19 Narratives That Keep Being Debunked: A Spatiotemporal Analysis
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Abstract (arXiv)
The onset of the COVID-19 pandemic led to a global infodemic that has brought unprecedented challenges for citizens, media, and fact-checkers worldwide. To address this challenge, over a hundred fact-checking initiatives worldwide have been monitoring the information space in their countries and publishing regular debunks of viral false COVID-19 narratives. This study examines the database of the CoronaVirusFacts Alliance, which contains 10,381 debunks related to COVID-19 published in multiple languages by different fact-checking organisations. Our spatiotemporal analysis reveals that similar or nearly duplicate false COVID-19 narratives have been spreading in multiple modalities and on various social media platforms in different countries, sometimes as much as several months after the first debunk of that narrative has been published by an International Fact-checking Network (IFCN) fact-checker. We also find that misinformation involving general medical advice has spread across multiple countries and hence has the highest proportion of false COVID-19 narratives that keep being debunked. Furthermore, as manual fact-checking is an onerous task in itself, therefore the need to repeatedly debunk the same narrative in different countries is leading, over time, to a significant waste of fact-checker resources. To this end, we propose the idea of including a multilingual debunk search tool in the fact-checking pipeline, in addition to recommending strongly that social media platforms need to adopt the same technology at scale, so as to make the best use of scarce fact-checker resources.
Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). First posted on arXiv 26 Jul 2021.
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Citing papers per year: 2022: 1; 2023: 1; 2024: 0; 2025: 1.
3 papers.
- 2023Breaking Language Barriers with MMTweets: Advancing Cross-Lingual Debunked Narrative Retrieval for Fact-Checkingcited by 5
- 2025Exploring Vision Language Models for Multimodal and Multilingual Stance Detectioncited by 3
- 2022GateNLP-UShef at SemEval-2022 Task 8: Entity-Enriched Siamese Transformer for Multilingual News Article Similaritycited by 1
- 2025Exploring Vision Language Models for Multimodal and Multilingual Stance Detection29 Jan 2025
- 2023Breaking Language Barriers with MMTweets: Advancing Cross-Lingual Debunked Narrative Retrieval for Fact-Checking10 Aug 2023
- 2022GateNLP-UShef at SemEval-2022 Task 8: Entity-Enriched Siamese Transformer for Multilingual News Article Similarity31 May 2022