Evidence map›Paper›PMID 40171439›Full record

ArticleFrontiers in public health2025

Health messages that engage audiences after the COVID-19 pandemic: content analysis of Chinese posts on social media.

Jing Yu, Yitong Zhao

Abstract read
In one paragraph

Article in Frontiers in public health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Article
  2. Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Jing YuSchool of Communication, East China Normal University, Shanghai, China.
Yitong ZhaoSchool of Politics and International Relations, East China Normal University, Shanghai, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: After China lifted strict COVID-19 control measures, the winter of 2023 saw widespread outbreaks of emerging infectious diseases, including various strains of influenza, mycoplasma pneumonia, and COVID-19. These diseases have heightened the risk of repeated infections and severe illness, making health communication strategies increasingly important for health promotion. While most studies focus on message dissemination by authorities and experts, the role of patient-generated content, shared by individuals with firsthand health experiences, remains underexplored. Methods: In this context, social media platforms like Xiaohongshu provide a new avenue for users to share personal health experiences, which have become an important aspect of health communication. This study aims to explore how health communicators can use different communication strategies to effectively engage audiences after the pandemic. By conducting a content analysis of 1,824 posts related to emerging infectious diseases on Xiaohongshu after the COVID-19 pandemic, we examine the relationship between health message strategies and audience engagement. Results: The results reveal that, in terms of message content, textual strategies such as information-focused language and source credibility cues positively impact audience engagement. In contrast, rich visual content negatively affects engagement. Regarding message style, strategies that include affective, interactive, and cohesive cues in text, as well as the use of warm colors in visuals, positively predict audience engagement. Conclusion: This study develops an integrated framework for health communicators to effectively use social media to share personal health experiences and engage audiences in collective responses to health crises.

Indexed as

COVID-19Health CommunicationHealth PromotionSocial MediaChinaHumansPandemicsSARS-CoV-2audience engagementcontent analysishealth communicationhealth message strategiessocial media

Identifiers

PMID40171439
PMCPMC11958945

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.