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Accepted for/Published in: Journal of Medical Internet Research

Date Submitted: May 27, 2021
Date Accepted: Aug 1, 2021
Date Submitted to PubMed: Aug 4, 2021

The final, peer-reviewed published version of this preprint can be found here:

Exploring the Expression Differences Between Professionals and Laypeople Toward the COVID-19 Vaccine: Text Mining Approach

Luo C, Ji K, Tang Y, Du Z

Exploring the Expression Differences Between Professionals and Laypeople Toward the COVID-19 Vaccine: Text Mining Approach

J Med Internet Res 2021;23(8):e30715

DOI: 10.2196/30715

PMID: 34346885

PMCID: 8404777

Exploring the Expression Differences Between Professionals and Laypeople Toward the COVID-19 Vaccine: A Text Mining Approach

  • Chen Luo; 
  • Kaiyuan Ji; 
  • Yulong Tang; 
  • Zhiyuan Du

ABSTRACT

Background:

COVID-19 is still rampant all over the world. Until now, the COVID-19 vaccine is the most promising measure to subdue contagion and achieve herd immunity. However, public vaccination intention is suboptimal. A clear division lies between the medical professionals and the laypeople. While most professionals eagerly promote the vaccination campaign, some laypeople exude suspicion, hesitancy, and even opposition toward COVID-19 vaccines.

Objective:

This study aims to employ a text mining approach to examine expression differences and thematic disparities between the professionals and laypeople within the COVID-19 vaccine context.

Methods:

We collect 3,196 answers under 65 filtered questions concerning the COVID-19 vaccine from a China-based Q&A forum named Zhihu. The questions were classified into five categories depending on their contents and description, including adverse reactions, vaccination, vaccine effectiveness, social implications of vaccine, and vaccine development. Respondents were also manually coded into two groups: professional and laypeople. Automated text analysis was performed to calculate fundamental expression characteristics of the two groups, containing answer length, attitude distribution, and high-frequency words. Besides, structural topic modeling (STM), as a cutting-edge branch in the topic modeling family, was utilized to extract topics under each question category, along with evaluating thematic disparities between the two groups.

Results:

Laypeople are more prevailing in the COVID-19 vaccine-related discussion. Regarding differences in expression characteristics, the professionals posted longer answers and showed a conservative stance towards vaccine effectiveness compared to laypeople. Laypeople mentioned countries more frequently, while professionals were inclined to raise medical jargon. STM discloses prominent topics under each question category. The statistical tests demonstrated that laypeople preferred the "safety of Chinese-made vaccine" topic and other vaccine-related issues in other nations of the world. However, the professionals paid more attention to medical principles and professional standards behind the COVID-19 vaccine. Respecting topics associated with the social implications of vaccines, the two groups showed no significant difference.

Conclusions:

Our findings indicate that laypeople and professionals share some common grounds but also hold divergent focuses toward the COVID-19 vaccine issue. Those incongruities can be summarized as "qualitatively different" in perspective rather than "quantitatively different" in scientific knowledge. Among those questions closely associated with medical expertise, the "qualitatively different" characteristic is quite conspicuous. This study boosts the current understanding of how the public perceives the COVID-19 vaccine in a more nuanced way. Online Q&A forum is a bonanza to investigate perception discrepancies among various identities. STM further exhibits unique strengths over the traditional topic modeling method in statistically testing the topic preference of various groups. Public health practitioners should be keenly aware of the cognitive differences between professionals and laypeople, also pay special attention to the topics with significant inconsistency across groups so as to build consensus and promote vaccination effectively.


 Citation

Please cite as:

Luo C, Ji K, Tang Y, Du Z

Exploring the Expression Differences Between Professionals and Laypeople Toward the COVID-19 Vaccine: Text Mining Approach

J Med Internet Res 2021;23(8):e30715

DOI: 10.2196/30715

PMID: 34346885

PMCID: 8404777

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