Singh, I., Bontcheva, K. orcid.org/0000-0001-6152-9600 and Scarton, C. orcid.org/0000-0002-0103-4072 (Submitted: 2021) The false COVID-19 narratives that keep being debunked : a spatiotemporal analysis. arXiv. (Submitted)
Abstract
The onset of the Coronavirus disease 2019 (COVID-19) pandemic instigated a global infodemic that has brought unprecedented challenges for society as a whole. During this time, a number of manual fact-checking initiatives have emerged to alleviate the spread of dis/mis-information. This study is about COVID-19 debunks published in multiple languages by different fact-checking organisations, sometimes as far as several months apart, despite the fact that the claim has already been fact-checked before. The spatiotemporal analysis reveals that similar or nearly duplicate false COVID-19 narratives have been spreading in multifarious modalities on various social media platforms in different countries. 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 debunking similar claims recurrently is leading to a waste of resources. To this end, we propound the idea of the inclusion of multilingual debunk search in the fact-checking pipeline.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2021 The Author(s). For reuse permissions, please contact the Author(s). |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Computer Science (Sheffield) |
Funding Information: | Funder Grant number European Commission - HORIZON 2020 825297; 871042 |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 27 Aug 2021 08:56 |
Last Modified: | 27 Aug 2021 08:56 |
Published Version: | https://arxiv.org/abs/2107.12303v2 |
Status: | Submitted |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:177508 |