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The Impact of Expert Opinions in COVID-19 News Coverage on Vaccination During the Fifth Outbreak in Hong Kong: A Time Series Analysis

Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review

Abstract

The role of experts in news coverage has become increasingly prominent, but the evidence regarding the effectiveness of expert opinions in affecting public behavior remains mixed. This study seeks to examine the influence of expert opinions covered in the news on the public's response to public health crises. By adopting a macro-level framing perspective, we investigated how framing consistency, a macro-level concept indicating the agreement between expert opinions in news coverage and government policies or among peer experts, evolves over time and its temporal causal relationship with public behavior. Specifically, this study collected all press news coverage in Hong Kong over four months during the fifth outbreak, including 1,416 articles with 650 expert opinions, as well as the vaccination data that paralleled with this period. We constructed time series of expert opinions and vaccination behavior, and then conducted Vector Autoregressive (VAR) models with Granger causality analysis to examine how framing consistency of expert opinions in news coverage influenced vaccination. The results indicate that the consistent framing between expert opinions and government policies increased COVID-19 vaccination during the fifth outbreak in Hong Kong, while conflicting opinions responding to government policies had no significant effect on vaccination. Opinions among medical experts on COVID-19 issues also did not significantly impact vaccination. The implications for designing communication strategies and enhancing public behavioral support during public health crises are discussed. © 2024 Taylor & Francis Group, LLC

Original languageEnglish
Pages (from-to)305-320
JournalHealth Communication
Volume40
Issue number2
Online published24 Apr 2024
DOIs
Publication statusPublished - 2025

Funding

This work was supported by the CityU Strategic Interdisciplinary Research Grant under Grant number [7020009].

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