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Perception of COVID-19 vaccination among Indian Twitter users: computational approach

Author

Listed:
  • Prateeksha Dawn Davidson

    (Model Rural Health Research Unit, ICMR)

  • Thanujah Muniandy

    (Meltwater)

  • Dhivya Karmegam

    (SRM Institute of Science and Technology)

Abstract

Vaccination has been a hot topic in the present COVID-19 context. The government, public health stakeholders and media are all concerned about how to get the people vaccinated. The study was intended to explore the perception and emotions of the Indians citizens toward COVID-19 vaccine from Twitter messages. The tweets were collected for the period of 6 months, from mid-January to June, 2021 using hash-tags and keywords specific to India. Topics and emotions from the tweets were extracted using Latent Dirichlet Allocation (LDA) method and National Research Council (NRC) Lexicon, respectively. Theme, sentiment and emotion wise engagement and reachability metrics were assessed. Hash-tag frequency of COVID-19 vaccine brands were also identified and evaluated. Information regarding ‘Co-WIN app and availability of vaccine’ was widely discussed and also received highest engagement and reachability among Twitter users. Among the various emotions, trust was expressed the most, which highlights the acceptance of vaccines among the Indian citizens. The hash-tags frequency of vaccine brands shows that Covishield was popular in the month of March 2021, and Covaxin in April 2021. The results from the study will help stakeholders to efficiently use social media to disseminate COVID-19 vaccine information on popular concerns. This in turn will encourage citizens to be vaccinated and achieve herd immunity. Similar methodology can be adopted in future to understand the perceptions and concerns of people in emergency situations.

Suggested Citation

  • Prateeksha Dawn Davidson & Thanujah Muniandy & Dhivya Karmegam, 2023. "Perception of COVID-19 vaccination among Indian Twitter users: computational approach," Journal of Computational Social Science, Springer, vol. 6(2), pages 541-560, October.
    • Handle: RePEc:spr:jcsosc:v:6:y:2023:i:2:d:10.1007_s42001-023-00203-0
    DOI: 10.1007/s42001-023-00203-0
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    1. Thilagavathi Ramamoorthy & Bagavandas Mappillairaju, 2023. "Tweet topics on cancer among Indian Twitter users—computational approach using latent Dirichlet allocation topic modelling," Journal of Computational Social Science, Springer, vol. 6(2), pages 1033-1054, October.

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