COVID-19 Vaccine-Related Information on the WeChat Public Platform: Topic Modeling and Content Analysis

Area of Science:

• Public Health
• Health Communication
• Social Media Analysis

Background:

• Social media significantly influences public trust and acceptance of COVID-19 vaccines.
• Effective dissemination of public health information relies on health behavior theories.
• Limited research exists on applying health behavior theories to COVID-19 vaccine messages on Chinese social media.

Purpose of the Study:

• To identify key topics and communication patterns of COVID-19 vaccine content on WeChat.
• To evaluate the application of health behavior theory, specifically the Health Belief Model (HBM), in these messages.

Main Methods:

• Systematic search of COVID-19 vaccine papers on the WeChat platform.
• Coding scheme based on the extended Health Belief Model (HBM) and analysis using NVivo 12.
• Topic modeling (Latent Dirichlet Allocation) and temporal analysis to track themes and HBM structures.

Main Results:

• Analysis of 757 papers revealed 5 main topics: vaccine development/effectiveness (35%), disease infection/protection (26%), vaccine access (18%), science popularization (14%), and safety/adverse reactions (7%).
• Most papers discussed solutions to obstacles (77%) and benefits (62%), while susceptibility (27%) and severity (18%) were less emphasized.
• All papers included at least one HBM component, but only 29 incorporated all structures; temporal analysis showed shifts in health belief structures post-vaccine market entry.

Conclusions:

• This study is the first to assess HBM application in COVID-19 vaccine information on WeChat.
• Identified communication characteristics and topic evolution before and after vaccine market entry.
• Findings can guide tailored educational and communication strategies for current and future pandemic vaccination efforts.