Vaccine Hesitancy and Anti-Vaccination Attitudes during the Start of COVID-19 Vaccination Program: A Content Analysis on Twitter Data

  1. Hüseyin Küçükali
  2. Ömer Ataç
  3. Ayşe Seval Palteki
  4. Ayşe Zülal Tokaç
  5. Osman Hayran

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Abstract

Twitter is a useful source for detecting anti-vaccine content due to the increasing prevalence of these arguments on social media. We aimed to identify the prominent themes about vaccine hesitancy and refusal on social media posts in Turkish during the COVID-19 pandemic. In this qualitative study, we collected public tweets (n = 551,245) that contained a vaccine-related keyword and had been published between 9 December 2020 and 8 January 2021 through the Twitter API. A random sample of tweets (n = 1041) was selected and analyzed by four researchers with the content analysis method. We found that 90.5% of the tweets were about vaccines, 22.6% (n = 213) of the tweets mentioned at least one COVID-19 vaccine by name, and the most frequently mentioned COVID-19 vaccine was CoronaVac (51.2%). We found that 22.0% (n = 207) of the tweets included at least one anti-vaccination theme. Poor scientific processes (21.7%), conspiracy theories (16.4%), and suspicions towards manufacturers (15.5%) were the most frequently mentioned themes. The most co-occurring themes were “poor scientific process” with “suspicion towards manufacturers” (n = 9), and “suspicion towards health authorities” (n = 5). This study may be helpful for health managers, assisting them to identify the major concerns of the population and organize preventive measures through the significant role of social media in early spread of information about vaccine hesitancy and anti-vaccination attitudes.

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  1. Version published to 10.3390/vaccines10020161
  2. ScreenIT

    SciScore for 10.1101/2021.05.28.21257774: (What is this?)

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

    Table 1: Rigor

    Ethicsnot detected.
    Sex as a biological variablenot detected.
    RandomizationTo be able to grasp potential daily differences among contents, a sample was selected by stratified random sampling proportioned to the daily number of tweets.
    Blindingnot detected.
    Power Analysisnot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    Data collected via software developed by a researcher in Python programming language using open-source libraries and Twitter Application Programming Interface (API) (12).
    Python
    suggested: (IPython, RRID:SCR_001658)

    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:
    A number of potential limitations need to be considered in our research. We actually conducted the content analysis in Turkish then translated to English. The meaning of the original tweets and the misspellings may be lost in translation. Also, this research included tweets in a specific time period as during the delivery of the first batch of vaccines to Turkey. On the other hand, it doesn’t include the time during vaccination. Our research is also limited by our keywords. Since we collected the tweets by our keywords, we might not catch complex versions of the vaccine’s name. Finally, our research was limited to Twitter. In conclusion, it is well known that vaccine hesitancy and anti-vaccination attitudes may negatively affect the population’s health, especially during a pandemic, and contents of the social media is an important source of early information about such attitudes. Analysis of the social media message contents may be helpful for health managers to identify the major issues and also to organize the preventive measures.

    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.

    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 10.1101/2021.05.28.21257774v1 on medRxiv