Evidence map›Paper›PMID 41019463›Full record

ArticleTobacco induced diseases2025

Do China's e-cigarette control policies work? A decade-long analysis of public discourse using an AI-integrated mixed-methods approach.

Zhangyan Li, Xinrui Wang, Xingye Yao, Yu Chen

Abstract read
In one paragraph

Article in Tobacco induced diseases, 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

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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

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3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

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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

4 authors.

Zhangyan Li *College of Media and International Culture, Zhejiang University, Hangzhou, China.
Xinrui Wang *School of Journalism, Communication University of China, Beijing, China.
Xingye Yao *Department of Software Development, Fujian Minguang Software Co., Ltd. Sanming, China.
Yu ChenSchool of Art and Communication, Fujian Polytechnic Normal University, Fuzhou, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionChina, the world's largest tobacco market, has raised concerns due to e-cigarettes' health risks and rising youth usage. Despite a decade of regulatory policies, their effectiveness remains uncertain. This study examines trends in e-cigarette discourse on Weibo (2016-2025), analyzing discussion volume shifts and the impact of various topics on public engagement.

methodsThis study employs a hybrid computational approach integrating topic modeling, LLM-assisted annotation, and quantitative analysis to examine the evolution of e-cigarette discussions on Weibo (2015-2025) and topic dissemination effects (n=129769). LDA modeling identify 10 topics, followed by DeepSeek-V3-assisted classification. Linear regression in SPSS analyzed relationships between topic categories and social media engagement metrics (reposts/comments/likes) at 95% confidence intervals.

resultsFindings reveal 2020 as a key year of change: pro-vaping posts declined while anti-vaping content increased. Despite reduced volume, pro-vaping material maintained significant digital influence. Pre-policy, marketing content (p<0.01), health effects (p<0.01) and regulation (p<0.01) drove engagement. Post-policy, marketing lost engagement impact, while 'user experience' posts gained traction, significantly correlating with all interactions (all p<0.05). This indicates regulations were less effective against user-generated content, with pro-vaping messaging shifting towards peer-driven channels. Crucially, influencers consistently triggered strong engagement throughout the period (p<0.01) despite lower post volume, remaining key discourse drivers.

conclusionsAlthough China is strengthening its control over e-cigarettes, the results of our study indicate that this control remains limited. We advocate for more robust regulation of social media content, particularly concerning the management of celebrities and influencers, as well as the sharing of e-cigarette use experiences. However, the current regulatory framework enforced by the State Tobacco Monopoly Administration has proven inadequate for widespread and effective governance. We suggest that regulatory authority be shared with public health agencies in order to better integrate e-cigarette regulation with broader public health objectives.

Indexed as

China’s policye-cigarette preventionmix-research methodtobacco control

Identifiers

PMID41019463
PMCPMC12465113

What OpenQuestion holds

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LicenceCC BY
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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.