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Public Perception of SARS-CoV-2 Vaccinations on Social Media: Questionnaire and Sentiment Analysis

Author

Listed:
  • Charlotte Roe

    (School of Life Sciences, University of Lincoln, Lincoln LN6 7TS, UK)

  • Madison Lowe

    (School of Life Sciences, University of Lincoln, Lincoln LN6 7TS, UK)

  • Benjamin Williams

    (School of Computer Science, University of Lincoln, Lincoln LN6 7TS, UK)

  • Clare Miller

    (School of Life Sciences, University of Lincoln, Lincoln LN6 7TS, UK)

Abstract

Vaccine hesitancy is an ongoing concern, presenting a major threat to global health. SARS-CoV-2 COVID-19 vaccinations are no exception as misinformation began to circulate on social media early in their development. Twitter’s Application Programming Interface (API) for Python was used to collect 137,781 tweets between 1 July 2021 and 21 July 2021 using 43 search terms relating to COVID-19 vaccines. Tweets were analysed for sentiment using Microsoft Azure (a machine learning approach) and the VADER sentiment analysis model (a lexicon-based approach), where the Natural Language Processing Toolkit (NLTK) assessed whether tweets represented positive, negative or neutral opinions. The majority of tweets were found to be negative in sentiment (53,899), followed by positive (53,071) and neutral (30,811). The negative tweets displayed a higher intensity of sentiment than positive tweets. A questionnaire was distributed and analysis found that individuals with full vaccination histories were less concerned about receiving and were more likely to accept the vaccine. Overall, we determined that this sentiment-based approach is useful to establish levels of vaccine hesitancy in the general public and, alongside the questionnaire, suggests strategies to combat specific concerns and misinformation.

Suggested Citation

  • Charlotte Roe & Madison Lowe & Benjamin Williams & Clare Miller, 2021. "Public Perception of SARS-CoV-2 Vaccinations on Social Media: Questionnaire and Sentiment Analysis," IJERPH, MDPI, vol. 18(24), pages 1-21, December.
    • Handle: RePEc:gam:jijerp:v:18:y:2021:i:24:p:13028-:d:699279
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    References listed on IDEAS

    as
    1. Viju Raghupathi & Jie Ren & Wullianallur Raghupathi, 2020. "Studying Public Perception about Vaccination: A Sentiment Analysis of Tweets," IJERPH, MDPI, vol. 17(10), pages 1-23, May.
    2. Elvira Ortiz-Sánchez & Almudena Velando-Soriano & Laura Pradas-Hernández & Keyla Vargas-Román & Jose L. Gómez-Urquiza & Guillermo A. Cañadas-De la Fuente & Luis Albendín-García, 2020. "Analysis of the Anti-Vaccine Movement in Social Networks: A Systematic Review," IJERPH, MDPI, vol. 17(15), pages 1-11, July.
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    Cited by:

    1. Yankang Su & Zbigniew J. Kabala, 2023. "Public Perception of ChatGPT and Transfer Learning for Tweets Sentiment Analysis Using Wolfram Mathematica," Data, MDPI, vol. 8(12), pages 1-27, November.
    2. Nazmiye Eligüzel, 2023. "Analyzing society anti-vaccination attitudes towards COVID-19: combining latent dirichlet allocation and fuzzy association rule mining with a fuzzy cognitive map," Fuzzy Optimization and Decision Making, Springer, vol. 22(4), pages 669-696, December.

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