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

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Abstract

Introduction

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, sparking optimism in the global financial markets. This study aims to explore the key themes from mainstream media’s coverage of COVID-19 vaccines on Facebook and examine how public interactions and responses on Facebook to mainstream media’s posts are associated with daily stock prices and trade volume of major vaccine manufacturers.

Methods

We obtained mainstream media’s coverage of COVID-19 vaccines and major vaccine manufacturers on Facebook from CrowdTangle, a public insights tool owned and operated by Facebook, as well as the corresponding trade volume and daily closing prices from January 2020 to December 2021. Structural topic modelling was used to analyze social media posts while regression analysis was conducted to determine the impact of Facebook reactions on stock prices and trade volume.

Results

10 diverse topics ranging from vaccine trials and their politicization (note: check that we use American spelling throughout), to stock market discussions were found to evolve over the pandemic. Although Facebook reactions were not consistently associated with vaccine manufacturers’ stock prices, ‘Haha’ and ‘Angry’ reactions showed the strongest association with stock price fluctuations. In comparison, social media reactions had little observable impact on trading volume.

Discussion

Topics generated reflect both actual events during vaccine development as well as its political and economic impact. The topics generated in this study reflect both the actual events surrounding vaccine development and its broader political and economic impact. While we anticipated a stronger correlation, our findings suggest a limited relationship between emotional reactions on Facebook and vaccine manufacturers’ stock prices and trading volume. We also discussed potential technical enhancements for future studies, including the integration of large language models.

Related collections

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All references

Author and article information

Contributors

Shun Yao Bai: URI : https://loop.frontiersin.org/people/2705085/overviewRole: Role: Role: Role: Role: Role: Role: Role: Role:

Edmund W. J. Lee: URI : https://loop.frontiersin.org/people/772677/overviewRole: Role: Role: Role: Role: Role:

Journal

Journal ID (nlm-ta): Front Public Health

Journal ID (iso-abbrev): Front Public Health

Journal ID (publisher-id): Front. Public Health

Title: Frontiers in Public Health

Publisher: Frontiers Media S.A.

ISSN (Electronic): 2296-2565

Publication date (Electronic): 13 August 2024

Publication date Collection: 2024

Volume: 12

Electronic Location Identifier: 1411345

Affiliations

[1] ¹College of Computing and Data Science, Nanyang Technological University , Singapore, Singapore

[2] ²Department of Media and Communication, City University of Hong Kong , Kowloon, Hong Kong SAR, China

Author notes

Edited by: Lei Hou, Nanjing University of Information Science and Technology, China

Reviewed by: Iuliana Raluca Gheorghe, Carol Davila University of Medicine and Pharmacy, Romania

Michele Marzulli, Ca’ Foscari University of Venice, Italy

*Correspondence: Shun Yao Bai, sbai005@ 123456e.ntu.edu.sg

Article

DOI: 10.3389/fpubh.2024.1411345

PMC ID: 11347277

PubMed ID: 39193202

SO-VID: 285706ac-5893-4d53-8a21-27ea77279edd

License:

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) and the copyright owner(s) 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.

History

Date received : 05 April 2024

Date accepted : 29 July 2024

Page count

Figures: 4, Tables: 5, Equations: 0, References: 49, Pages: 11, Words: 7004

Funding

The author(s) declare that financial support was received for the research, authorship, and/or publication of this article. This project was supported by Nanyang Technological University (NTU) under the URECA Undergraduate Research Programme as well as NTU’s Start Up Grant (grant number: 020154-00001).

Custom metadata

section-at-acceptance Health Economics

Keywords: covid-19,coronavirus,stock market,social media,sentiment analysis,topic modelling

Data availability:

Keywords: covid-19, coronavirus, stock market, social media, sentiment analysis, topic modelling