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ORIGINAL RESEARCH article

Front. Public Health
Sec. Health Economics
Volume 12 - 2024 | doi: 10.3389/fpubh.2024.1411345

Examining media's coverage of COVID-19 vaccines and social media sentiments on vaccine manufacturers' stock prices

Provisionally accepted
Shun Yao Bai Shun Yao Bai 1*Edmund W. Lee Edmund W. Lee 2
  • 1 Nanyang Technological University, Singapore, Singapore
  • 2 City University of Hong Kong, Kowloon, Hong Kong, SAR China

The final, formatted version of the article will be published soon.

    The COVID-19 pandemic caused a widespread public health and financial crisis. The rapid vaccine development generated extensive discussions in both mainstream and social media, and sparked optimism in the global financial markets. This study aims to (a) explore the key themes from mainstream media's coverage of COVID-19 vaccines on Facebook, and (b) examines how public interactions and responses on Facebook to mainstream media's posts are associated to daily stock prices and trade volume of major vaccine manufacturers. We obtained mainstream media's coverage of COVID-19 vaccines and major vaccine manufacturers on Facebook, as well as the corresponding trade volume and daily closing prices from January 2020 to December 2021. Structural topic modelling on social media posts revealed ten distinct topics evolving over the pandemic, covering areas such as vaccine trials, politicization, and stock market discussions. Regression analysis showed that Facebook reactions had inconsistent effects on stock prices, with specific reactions 'Haha' and 'Angry' having the most discernible impact on stock prices of major vaccine manufacturers. However, social media reactions had very little observable impact on trade volume. Theoretical and practical insights into the complex interplay between public sentiment and financial markets were discussed.

    Keywords: COVID-19, Coronavirus, Stock Market, Social Media, sentiment analysis, Topic modelling

    Received: 05 Apr 2024; Accepted: 29 Jul 2024.

    Copyright: © 2024 Bai and Lee. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Shun Yao Bai, Nanyang Technological University, Singapore, Singapore

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.