COVID-19-Related Social Media Fake News in India

  1. Md. Sayeed Al-Zaman

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Abstract

COVID-19-related online fake news poses a threat to Indian public health. In response, this study seeks to understand the five important features of COVID-19-related social media fake news by analyzing 125 Indian fake news. The analysis produces five major findings based on five research questions. First, the seven themes of fake news are health, religiopolitical, political, crime, entertainment, religious, and miscellaneous. Health-related fake news (67.2%) is on the top of the list that includes medicine, medical and healthcare facilities, viral infection, and doctor-patient issues. Second, the seven types of fake news contents are text, photo, audio, video, text and photo, text and video, and text and photo and video. More fake news takes the form of text and video (47.2%). Third, online media produces more fake news (94.4%) than mainstream media (5.6%). More interestingly, four social media platforms: Twitter, Facebook, WhatsApp, and YouTube, produce most of the fake news. Fourth, relatively more fake news has international connections (54.4%) as the COVID-19 pandemic is a global phenomenon. Fifth, most of the COVID-19-related fake news is negative (63.2%) which could be a real threat to public health. These results may contribute to the academic understanding of social media fake news during the present and future health-crisis period. This paper concludes by stating some limitations regarding the data source and results, as well as provides a few suggestions for further research.

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  1. Version published to 10.3390/journalmedia2010007
  2. ScreenIT

    SciScore for 10.1101/2020.07.06.20147066: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board StatementConsent: They resolved the coding issues and complexities based on mutual consent and completed the codebook (Krippendorff, 2013).
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    Apart from analog media content (that often become popular in social media), user-generated content (UGC) from the internet are used widely in research purposes nowadays (Bordens & Abbott, 2017).
    Abbott
    suggested: (Abbott, RRID:SCR_010477)

    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: We detected the following sentences addressing limitations in the study:
    Apart from some exclusive findings, this study has a few limitations as well. The collected data are from Indian fake news cases and the country has a few distinctive natures from the aspects of political ambiance, cultural exceptionalities, technological penetration, communication, and news consumption patterns. These might elevate Indian fake news problems to another level that could be more relatable to Bangladesh and Pakistan, and more different from others, such as the USA and the UK (Al-Zaman, 2019, 2020). Also, the collected data are not cross-cultural and perhaps limited in number that might cause a generalization-problem. Moreover, a knowledge-gap still exists that has not been bridged yet: why COVID-19-related fake news in social media increases or decreases? After all its limitations, this study is a unique contribution to the COVID-19 fake news researches that identifies and bridges a few gaps in the existing literature. Besides, it explores further gaps that invite more researchers in this area.

    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 10.1101/2020.07.06.20147066v1 on medRxiv