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Accepted for/Published in: JMIR Infodemiology

Date Submitted: Feb 2, 2022
Date Accepted: Jul 18, 2022

The final, peer-reviewed published version of this preprint can be found here:

Investigation of COVID-19 Misinformation in Arabic on Twitter: Content Analysis

AL-RAWI A, Fakida A, Grounds K

Investigation of COVID-19 Misinformation in Arabic on Twitter: Content Analysis

JMIR Infodemiology 2022;2(2):e37007

DOI: 10.2196/37007

PMID: 35915823

PMCID: 9327499

Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.

Investigating COVID-19 misinformation in Arabic on Twitter

  • AHMED AL-RAWI; 
  • Abdelrahman Fakida; 
  • Kelly Grounds

ABSTRACT

This study empirically examines the way Arabic speakers use specific hashtags on Twitter to express anti-vaccine and anti-pandemic views. By exploring this topic, we aim at filling a gap in literature that can help in understanding Arabic language conspiracies around Covid-19. After downloading a large dataset from Twitter, we content analyzed the most retweeted posts and found that users mostly discuss three specific topics. First, the topic of infringing on civil liberties (43.5%) covers ways that governments have allegedly infringed on civil liberties during the pandemic and unfair restrictions that have been imposed on unvaccinated individuals. This was followed by five varieties of vaccine-related conspiracies (37%), including a Deep State dictating pandemic polices, mistrusting vaccine efficacy, and/or discussing unproven treatments. Finally, calls-for-action (13%) encourage individuals to participate in civil demonstrations. For each of these topics, we also explored the inner logic underpinning some claims, taking examples from prominent conspiracy theories. The implications of the study are discussed in the conclusion and ways to expand the current research are identified.


 Citation

Please cite as:

AL-RAWI A, Fakida A, Grounds K

Investigation of COVID-19 Misinformation in Arabic on Twitter: Content Analysis

JMIR Infodemiology 2022;2(2):e37007

DOI: 10.2196/37007

PMID: 35915823

PMCID: 9327499

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