Evidence map›Paper›PMID 42436469›Full record

ArticleBMC public health2026

Health anxiety and collective sense-making in the Chinese social media sphere: a topic modeling assessment of Weibo chatters.

Lixia Peng, Zixue Tai, Xiaolong Liu

Abstract read
In one paragraph

Article in BMC public health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

3 authors.

Lixia PengSchool of Journalism and Communication, Minjiang University, 200, Xiyuangong Road, Fuzhou, 350108, Fujian Province, China.ORCID http://orcid.org/0009-0003-9869-2788
Zixue TaiSchool of Journalism and Media, University of Kentucky, 507 Library Drive, Lexington, KY, 40506, USA.ORCID http://orcid.org/0000-0002-2007-7692
Xiaolong LiuSchool of Marxism, Guangdong Pharmaceutical University, 280 Waihuan Road East, Guangzhou, 510006, Guangdong Province, China. liuxiaolong@gdpu.edu.cn.ORCID http://orcid.org/0009-0007-3657-1963

Funding

Natural Science Foundation of Fujian Province of China No. 2025J08286Research Project of Fashu Foundation No. MFS24017
6 · The paper itself

Abstract

backgroundThe pervasive penetration of social media is reshaping the generative logic and communication pathways of health anxiety, which is significantly impacting public mental health and individual behavior. This study aimed to examine the topic threads of health anxiety discussions on Sina Weibo as well as the threat and efficacy elements contained in the health-related messages through the Extended Parallel Process Model (EPPM) framework.

methodsA total of 138,587 social media posts related to health anxiety were collected using keyword combination of "health" AND ("worry" OR "concern" OR "anxiety") on the Weibo open API. Latent Dirichlet Allocation (LDA) model procedures were applied to identify the main topics in health anxiety discussions. Moreover, 1,480 most popular posts ranked by "Like" clicks were selected for manual textual analysis. Based on the Extended Parallel Process Model (EPPM), four dimensions-severity, susceptibility, self-efficacy, and response efficacy-were used as the coding framework for content analysis to systematically ascertain the topic structure, information characteristics and information engagement with health anxiety Weibo discussions.

resultsDiscussions of health anxiety cover a wide range of topics. The most prominent topic is positive living and health ideals (intensity = 0.4456), and other major topics include sleep disorders and mental health, adolescent mental health education and social adaptation, healthy lifestyle and self-management. There is a noticeable imbalance between elements of threat and efficacy in health anxiety discussions. Threat information dominates health anxiety discussions, with severity being the most prevalent component (73.24%). Conversely, efficacy information is much less featured, particularly regarding response efficacy (16.15%). Higher perceived threat levels in health anxiety discussions correlate with reduced engagement (fewer likes), whereas greater efficacy information levels are associated with increased engagement (more likes).

conclusionsHealth anxiety discussions on social media exhibit a generative logic characterized by thematic threads emphasizing positive confidence-building, proactive measures, ailment-specific guidance, and subgroup-focused solution tips. Users' conception of health anxiety reflects a contradictory pattern of "high threat-low efficacy", wherein information about health threats is far more prevalent than information about self-efficacy. These findings show that EPPM-related threat and efficacy elements are distributed in user-generated Weibo discussions.

Indexed as

AnxietySocial MediaChinaHumansContent analysisEPPMExtended Parallel Process ModelHealth anxietyLatent Dirichlet AllocationLDA topic modelWeibo

Identifiers

PMID42436469
PMCPMC13640346

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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.