Content Analysis and Characterization of Medical Tweets During the Early Covid-19 Pandemic

  1. Ross Prager
  2. Michael T Pratte
  3. Rudy R Unni
  4. Sudarshan Bala
  5. Nicholas Ng Fat Hing
  6. Kay Wu
  7. Trevor A McGrath
  8. Adam Thomas
  9. Brent Thoma
  10. Kwadwo Kyeremanteng

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Abstract

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  1. Version published to 10.7759/cureus.13594
  2. ScreenIT

    SciScore for 10.1101/2020.12.22.20248712: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    Institutional Review Board Statementnot detected.
    RandomizationThe profiles were randomized and selected using the randomize function in Microsoft Excel.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    Data analysis: We saved the extracted data in Microsoft Excel 2013 (Washington, USA) and analyzed it using R version 3.6.2 (R Project for Statistical Computing).
    Microsoft Excel
    suggested: (Microsoft Excel, RRID:SCR_016137)
    R Project for Statistical
    suggested: (R Project for Statistical Computing, RRID:SCR_001905)

    Results from OddPub: Thank you for sharing your data.

    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    Their criticisms were substantiated with recent studies showing increased adverse events and a potential association with mortality for hydroxychloroquine.[26,27] Contributing to and participating in the FOAM community is not without risks and the unwritten rule is caveat emptor (buyer beware). Across social media, the potential for receiving misinformation is real and significant.[28] In this study, 19 contributors contributed blatantly false or misleading information; however, this represented only 0·2% of the total number of tweets in this analysis. Whereas blatantly misleading tweets are relatively easy to identify, a significant concern is when a reader is misled through either misrepresentation of opinion as fact, sensational anecdotes, or providing content without context. A recent study Kouzy et al. found that 1 in 4 tweets about Covid-19 across Twitter (no FOAM hashtags) contained misinformation.[29] We suspect the rate of subtle misinformation in the FOAM community is higher than the 0·2% found in this study; however, given that the community’s collective goal is to share legitimate knowledge, it is likely lower than the broader Twitter community. FOAM has the potential to decrease the knowledge translation gap during Covid-19; however, resources may be of variable quality.[30] Readers are responsible for critically appraising online content; however, locating quality resources to begin with can be a challenge. The Social Media Index (SMI) provides a list of FOAM we...

    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.

    About SciScore

    SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

  3. Version published to 10.1101/2020.12.22.20248712v1 on medRxiv