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2018 - 2022年中国社交媒体平台上牙齿保健信息的演变趋势:回顾性观察研究

Evolutionary Trend of Dental Health Care Information on Chinese Social Media Platforms During 2018-2022: Retrospective Observational Study.

作者信息

Zhu Zhiyu, Ye Zhiyun, Wang Qian, Li Ruomei, Li Hairui, Guo Weiming, Li Zhenxia, Xia Lunguo, Fang Bing

机构信息

Department of Orthodontics, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.

College of Stomatology, Shanghai Jiao Tong University, National Center for Stomatology, National Clinical Research Center for Oral Diseases, Shanghai Key Laboratory of Stomatology, Shanghai Research Institute of Stomatology, Shanghai, China.

出版信息

JMIR Infodemiology. 2025 Apr 10;5:e55065. doi: 10.2196/55065.


DOI:10.2196/55065
PMID:40209216
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC12022532/
Abstract

BACKGROUND: Social media holds an increasingly significant position in contemporary society, wherein evolving public perspectives are mirrored by changing information. However, there remains a lack of comprehensive analysis regarding the nature and evolution of dental health care information on Chinese social media platforms (SMPs) despite extensive user engagement and voluminous content. OBJECTIVE: This study aimed to probe into the nature and evolution of dental health care information on Chinese SMPs from 2018 to 2022, providing valuable insights into the evolving digital public perception of dental health for dental practitioners, investigators, and educators. METHODS: This study was conducted on 3 major Chinese SMPs: Weibo, WeChat, and Zhihu. Data from March 1 to 31 in 2018, 2020, and 2022 were sampled to construct a social media original database (ODB), from which the most popular long-text posts (N=180) were selected to create an analysis database (ADB). Natural language processing (NLP) tools were used to assist tracking topic trends, and word frequencies were analyzed. The DISCERN health information quality assessment questionnaire was used for information quality evaluation. RESULTS: The number of Weibo posts in the ODB increased approximately fourfold during the observation period, with discussion of orthodontic topics showing the fastest growth, surpassing that of general dentistry after 2020. In the ADB, the engagement of content on Weibo and Zhihu also displayed an upward trend. The overall information quality of long-text posts on the 3 platforms was moderate or low. Of the long-text posts, 143 (79.4%) were written by nonprofessionals, and 105 (58.3%) shared personal medical experiences. On Weibo and WeChat, long-text posts authored by health care professionals had higher DISCERN scores (Weibo P=.04; WeChat P=.02), but there was a negative correlation between engagement and DISCERN scores (Weibo tau-b [τb]=-0.45, P=.01; WeChat τb=-0.30, P=.02). CONCLUSIONS: There was a significant increase in the dissemination and evolution of public interest in dental health care information on Chinese social media during 2018-2022. However, the quality of the most popular long-text posts was rated as moderate or low, which may mislead patients and the public.

摘要

背景:社交媒体在当代社会中占据着日益重要的地位,公众观点的演变通过信息的变化得以体现。然而,尽管中国社交媒体平台(SMP)上用户参与度高且内容丰富,但对于牙科保健信息的性质和演变仍缺乏全面分析。 目的:本研究旨在探究2018年至2022年中国社交媒体平台上牙科保健信息的性质和演变,为牙科从业者、研究人员和教育工作者提供有关数字时代公众对牙科健康认知演变的宝贵见解。 方法:本研究针对中国三大社交媒体平台进行:微博、微信和知乎。对2018年、2020年和2022年3月1日至31日的数据进行抽样,构建社交媒体原始数据库(ODB),并从中选取最热门的长文帖子(N = 180)创建分析数据库(ADB)。使用自然语言处理(NLP)工具辅助跟踪话题趋势,并分析词频。采用DISCERN健康信息质量评估问卷进行信息质量评估。 结果:在观察期内,ODB中微博帖子数量增加了约四倍,正畸话题的讨论增长最快,2020年后超过了普通牙科话题。在ADB中,微博和知乎上内容的参与度也呈上升趋势。三个平台上长文帖子的整体信息质量为中等或较低。在长文帖子中,143篇(79.4%)由非专业人士撰写,105篇(58.3%)分享个人医疗经历。在微博和微信上,医疗保健专业人员撰写的长文帖子的DISCERN得分更高(微博P = 0.04;微信P = 0.02),但参与度与DISCERN得分之间存在负相关(微博tau - b [τb] = -0.45,P = 0.01;微信τb = -0.30,P = 0.02)。 结论:2018 - 2022年期间,中国社交媒体上公众对牙科保健信息的关注度传播和演变显著增加。然而,最热门长文帖子的质量被评为中等或较低,这可能会误导患者和公众。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3454/12022532/bc4e511a3171/infodemiology_v5i1e55065_fig6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3454/12022532/7ba899e09af0/infodemiology_v5i1e55065_fig1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3454/12022532/2248a4eb8934/infodemiology_v5i1e55065_fig2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3454/12022532/b9669d33a2ad/infodemiology_v5i1e55065_fig3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3454/12022532/2cb7d08debd2/infodemiology_v5i1e55065_fig4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3454/12022532/45dbd7a9b467/infodemiology_v5i1e55065_fig5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3454/12022532/bc4e511a3171/infodemiology_v5i1e55065_fig6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3454/12022532/7ba899e09af0/infodemiology_v5i1e55065_fig1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3454/12022532/2248a4eb8934/infodemiology_v5i1e55065_fig2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3454/12022532/b9669d33a2ad/infodemiology_v5i1e55065_fig3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3454/12022532/2cb7d08debd2/infodemiology_v5i1e55065_fig4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3454/12022532/45dbd7a9b467/infodemiology_v5i1e55065_fig5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3454/12022532/bc4e511a3171/infodemiology_v5i1e55065_fig6.jpg

相似文献

[1]
Evolutionary Trend of Dental Health Care Information on Chinese Social Media Platforms During 2018-2022: Retrospective Observational Study.

JMIR Infodemiology. 2025-4-10

[2]
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[3]
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[4]
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[5]
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[6]
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J Med Internet Res. 2020-11-26

[7]
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J Med Internet Res. 2018-8-9

[8]
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[9]
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[10]
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本文引用的文献

[1]
Are Social Media Platforms Appropriate Sources of Information for Patients Regarding the Topic of Facial Trauma?

J Oral Maxillofac Surg. 2023-10

[2]
Popular Tag Recommendation by Neural Network in Social Media.

Comput Intell Neurosci. 2023

[3]
Artificial Intelligence-Enabled Analysis of Statin-Related Topics and Sentiments on Social Media.

JAMA Netw Open. 2023-4-3

[4]
Orthodontic treatment with miniscrew anchorage: Analysis of quality of information on YouTube.

Am J Orthod Dentofacial Orthop. 2023-7

[5]
Using GPT-3 to Build a Lexicon of Drugs of Abuse Synonyms for Social Media Pharmacovigilance.

Biomolecules. 2023-2-18

[6]
Social media and misinformation in diabetes and obesity.

Lancet Diabetes Endocrinol. 2022-12

[7]
COVID-19, climate change, and the finite pool of worry in 2019 to 2021 Twitter discussions.

Proc Natl Acad Sci U S A. 2022-10-25

[8]
Exploring Coronavirus Disease 2019 Vaccine Hesitancy on Twitter Using Sentiment Analysis and Natural Language Processing Algorithms.

Clin Infect Dis. 2022-5-15

[9]
Reducing "COVID-19 Misinformation" While Preserving Free Speech.

JAMA. 2022-4-19

[10]
TikTok and Attention-Deficit/Hyperactivity Disorder: A Cross-Sectional Study of Social Media Content Quality.

Can J Psychiatry. 2022-12

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