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中国小红书上用户生成帖子中的电子烟叙事:内容分析

E-Cigarette Narratives of User-Generated Posts on Xiaohongshu in China: Content Analysis.

作者信息

Ji Tingfen, Liu Zhao, Su Zheng, Xia Xin, Liu Yi, Xie Ying, Huang Zhenxiao, Zhou Xinmei, Wang Min, Cheng Anqi, Song Qingqing, Shi Yuxin, Shi Shunyi, Ailifeire Aihemaiti, He Jiahui, Gao Yingman, Zhao Liang, Wu Liyan, Xiao Dan, Wang Chen

机构信息

China-Japan Friendship School of Clinical Medicine, Capital Medical University, Beijing, China.

Department of Tobacco Control and Prevention of Respiratory Diseases, Center of Respiratory Medicine, China-Japan Friendship Hospital, Beijing, China, 86 010 8420 5288, 86 010-64217749.

出版信息

J Med Internet Res. 2025 Jul 3;27:e71173. doi: 10.2196/71173.


DOI:10.2196/71173
PMID:40609075
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC12244743/
Abstract

BACKGROUND: Social media platforms have become influential spaces for disseminating information about electronic cigarettes (e-cigarettes). Concerns persist about the spread of misleading content, particularly among social media vulnerable groups. Xiaohongshu (RedNote), widely used by Chinese youth, plays a growing role in shaping e-cigarette perceptions. Understanding the narratives circulating on this platform is essential for identifying misinformation, assessing public perception, and guiding future health communication strategies. OBJECTIVE: This study aimed to analyze the content, topics, user engagement, and sentiment trends of e-cigarette-related posts on Xiaohongshu and to assess the factors that influence engagement. METHODS: E-cigarette-related posts published on Xiaohongshu between January 2020 and November 2024 were collected using web scraping, based on a predefined keyword list and a time-stratified random sampling strategy. Posts were categorized into 4 themes: advertising promotion, health hazards, usage interaction, and others. High-frequency keywords were extracted, and representative quotes were included to illustrate user perspectives across each category. Sentiment analysis was performed on posts in the usage interaction category to assess public attitudes. We defined 4 sentiment categories: positive, negative, neutral, and mixed. Logistic regression was conducted to explore the effects of post type, content length, and thematic classification on user engagement metrics such as likes, saves, and comments. RESULTS: A total of 1729 posts were included and analyzed. Usage interaction posts were the most common (681/1729, 39.39%), with keywords such as "experience," "regulations," and "quit smoking" dominating this category. Advertising promotion posts (512/1729, 29.61%) frequently used terms like "flavor," "fashion," and "design" to attract younger users. Health hazards posts (311/1729, 17.99%) highlighted risks with keywords like "nicotine," "addiction," and "secondhand smoke," while others included policy and industry updates. Representative quotes highlighted typical concerns about aesthetics, health risks, and cessation struggles. Health hazards posts garnered the highest engagement in terms of likes and saves, despite their limited presence (odds ratio [OR] 1.498, 95% CI 1.099-2.042, P=.01). Video posts significantly outperformed text-image posts in generating comments (OR 2.624, 95% CI 2.017-3.439, P<.001). Sentiment analysis of the usage interaction posts (n=681) revealed that 53.45% (364/681) were positive, highlighting reduced harm, convenience, or flavor preferences. Negative sentiment was observed in 33.48% (228/681) of posts, often expressing concerns about addiction and health risks. Mixed sentiments appeared in 6.90% (47/681), acknowledging both pros and cons. In addition, 6.17% (42/681) of posts were classified as neutral without evident emotional tone. CONCLUSIONS: The findings underscore the dual role of Xiaohongshu as a platform for both e-cigarette promotion and public discourse. Misleading marketing targeting vulnerable groups, such as adolescents, remains a critical issue. However, the strong user response to health-related content suggests that social media platforms could be leveraged for effective health education. Strengthened regulatory oversight and educational campaigns leveraging engaging content formats are urgently needed to counter misinformation and protect public health.

摘要

背景:社交媒体平台已成为传播电子烟信息的重要空间。对于误导性内容的传播,尤其是在社交媒体弱势群体中的传播,人们一直存在担忧。小红书在中国青年中广泛使用,在塑造电子烟认知方面发挥着越来越重要的作用。了解该平台上流传的叙述对于识别错误信息、评估公众认知以及指导未来的健康传播策略至关重要。 目的:本研究旨在分析小红书上与电子烟相关帖子的内容、主题、用户参与度和情感趋势,并评估影响参与度的因素。 方法:基于预定义的关键词列表和时间分层随机抽样策略,通过网络爬虫收集2020年1月至2024年11月在小红书上发布的与电子烟相关的帖子。帖子分为4个主题:广告推广、健康危害、使用互动和其他。提取高频关键词,并纳入代表性引语以说明每个类别中的用户观点。对使用互动类别中的帖子进行情感分析,以评估公众态度。我们定义了4种情感类别:积极、消极、中性和混合。进行逻辑回归以探讨帖子类型、内容长度和主题分类对点赞、收藏和评论等用户参与度指标的影响。 结果:共纳入并分析了1729篇帖子。使用互动类帖子最为常见(681/1729,39.39%),该类别中“体验”“规定”和“戒烟”等关键词占主导。广告推广类帖子(512/1729,29.61%)经常使用“口味”“时尚”和“设计”等词汇来吸引年轻用户。健康危害类帖子(311/1729,17.99%)用“尼古丁”“成瘾”和“二手烟”等关键词突出风险,其他类别包括政策和行业更新。代表性引语突出了对美观、健康风险和戒烟困难的典型担忧。尽管健康危害类帖子数量有限,但在点赞和收藏方面获得的参与度最高(优势比[OR]1.498,95%置信区间1.099 - 2.042,P = 0.01)。视频帖子在产生评论方面显著优于图文帖子(OR 2.624,95%置信区间2.017 - 3.439,P < 0.001)。对使用互动类帖子(n = 681)的情感分析显示,53.45%(364/68所)为积极,强调危害降低、便利性或口味偏好。33.48%(228/681)的帖子观察到消极情绪,常表达对成瘾和健康风险的担忧。6.90%(47/681)出现混合情绪,承认利弊皆有。此外,6.17%(42/681)的帖子被归类为中性,没有明显的情感倾向。 结论:研究结果强调了小红书作为电子烟推广和公众讨论平台的双重作用。针对青少年等弱势群体的误导性营销仍然是一个关键问题。然而,用户对健康相关内容的强烈反应表明,社交媒体平台可用于有效的健康教育。迫切需要加强监管监督,并利用引人入胜的内容形式开展教育活动,以对抗错误信息并保护公众健康。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9ad4/12244743/652401c812c7/jmir-v27-e71173-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9ad4/12244743/cd01de4a379c/jmir-v27-e71173-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9ad4/12244743/2c9a36181da4/jmir-v27-e71173-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9ad4/12244743/12c42778fb10/jmir-v27-e71173-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9ad4/12244743/652401c812c7/jmir-v27-e71173-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9ad4/12244743/cd01de4a379c/jmir-v27-e71173-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9ad4/12244743/2c9a36181da4/jmir-v27-e71173-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9ad4/12244743/12c42778fb10/jmir-v27-e71173-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9ad4/12244743/652401c812c7/jmir-v27-e71173-g004.jpg

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本文引用的文献

[1]
Public Response to Federal Electronic Cigarette Regulations Analyzed Using Social Media Data Through Natural Language Processing: Topic Modeling Study.

J Med Internet Res. 2024-10-1

[2]
Perceptions of and responses of young adults who use e-cigarettes to flavour bans in China: a qualitative study.

Tob Control. 2024-1-24

[3]
A Critical Review of E-Cigarette Regulation in China: Challenges and Prospects for Youth Prevention and Tobacco Control.

Nicotine Tob Res. 2024-1-22

[4]
Milestones in the natural course of the onset of e-cigarette dependence among adolescents and young adults: Retrospective study.

Addict Behav. 2024-1

[5]
Social Media Use and Subsequent E-Cigarette Susceptibility, Initiation, and Continued Use Among US Adolescents.

Prev Chronic Dis. 2023-9-7

[6]
Medicalisation of vaping in the UK? E-cigarette users' perspectives on the merging of commercial and medical routes to vaping.

Perspect Public Health. 2023-8-6

[7]
The history, evolution, and practice of cannabis and E-cigarette industries highlight necessary public health and public safety considerations.

J Safety Res. 2023-2

[8]
Comparison of the Users' Attitudes Toward Cannabidiol on Social Media Platforms: Topic Modeling Study.

JMIR Public Health Surveill. 2023-1-11

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Acta Psychol (Amst). 2022-10

[10]
E-Commerce Brand Ranking Algorithm Based on User Evaluation and Sentiment Analysis.

Front Psychol. 2022-6-23

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