Prevalence and source analysis of COVID-19 misinformation in 138 countries
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Abstract
This study analysed 9657 pieces of misinformation that originated in 138 countries and were fact-checked by 94 organizations to understand the prevalence and sources of misinformation in different countries. The results show that India (15.94%), the USA (9.74%), Brazil (8.57%) and Spain (8.03%) are the four most misinformation-affected countries. Based on the results, it is presumed that the prevalence of COVID-19 misinformation can have a positive association with the COVID-19 situation. Social media (84.94%) produces the largest amount of misinformation, and the Internet (90.5%) as a whole is responsible for most of the COVID-19 misinformation. Moreover, Facebook alone produces 66.87% of the misinformation among all social media platforms. Of all the countries, India (18.07%) produced the largest amount of social media misinformation, perhaps thanks to the country’s higher Internet penetration rate, increasing social media consumption and users’ lack of Internet literacy.
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SciScore for 10.1101/2021.05.08.21256879: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Ethics Consent: Also, unlike semi-public and private data, our data did not require informed consent (13). Sex as a biological variable not detected. Randomization not detected. Blinding not detected. Power Analysis not detected. Table 2: Resources
Software and Algorithms Sentences Resources For data preparation and analysis, we used Microsoft Excel 2019 and IBM SPSS Statistics 25. Microsoft Excelsuggested: (Microsoft Excel, RRID:SCR_016137)SPSSsuggested: (SPSS, RRID:SCR_002865)Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: An explicit section about the …SciScore for 10.1101/2021.05.08.21256879: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Ethics Consent: Also, unlike semi-public and private data, our data did not require informed consent (13). Sex as a biological variable not detected. Randomization not detected. Blinding not detected. Power Analysis not detected. Table 2: Resources
Software and Algorithms Sentences Resources For data preparation and analysis, we used Microsoft Excel 2019 and IBM SPSS Statistics 25. Microsoft Excelsuggested: (Microsoft Excel, RRID:SCR_016137)SPSSsuggested: (SPSS, RRID:SCR_002865)Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.Results from TrialIdentifier: No clinical trial numbers were referenced.
Results from Barzooka: We did not find any issues relating to the usage of bar graphs.
Results from JetFighter: We did not find any issues relating to colormaps.
Results from rtransparent:- Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
- Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
- No protocol registration statement was detected.
Results from scite Reference Check: We found no unreliable references.
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