Nanfang Hospital, Southern Medical University, Guangzhou, China.
School of Nursing, Southern Medical University, Guangzhou, China.
J Med Internet Res. 2023 Jun 26;25:e43349. doi: 10.2196/43349.
Given the rapid development of social media, effective extraction and analysis of the contents of social media for health care have attracted widespread attention from health care providers. As far as we know, most of the reviews focus on the application of social media, and there is a lack of reviews that integrate the methods for analyzing social media information for health care.
This scoping review aims to answer the following 4 questions: (1) What types of research have been used to investigate social media for health care, (2) what methods have been used to analyze the existing health information on social media, (3) what indicators should be applied to collect and evaluate the characteristics of methods for analyzing the contents of social media for health care, and (4) what are the current problems and development directions of methods used to analyze the contents of social media for health care?
A scoping review following Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines was conducted. We searched PubMed, the Web of Science, EMBASE, the Cumulative Index to Nursing and Allied Health Literature, and the Cochrane Library for the period from 2010 to May 2023 for primary studies focusing on social media and health care. Two independent reviewers screened eligible studies against inclusion criteria. A narrative synthesis of the included studies was conducted.
Of 16,161 identified citations, 134 (0.8%) studies were included in this review. These included 67 (50.0%) qualitative designs, 43 (32.1%) quantitative designs, and 24 (17.9%) mixed methods designs. The applied research methods were classified based on the following aspects: (1) manual analysis methods (content analysis methodology, grounded theory, ethnography, classification analysis, thematic analysis, and scoring tables) and computer-aided analysis methods (latent Dirichlet allocation, support vector machine, probabilistic clustering, image analysis, topic modeling, sentiment analysis, and other natural language processing technologies), (2) categories of research contents, and (3) health care areas (health practice, health services, and health education).
Based on an extensive literature review, we investigated the methods for analyzing the contents of social media for health care to determine the main applications, differences, trends, and existing problems. We also discussed the implications for the future. Traditional content analysis is still the mainstream method for analyzing social media content, and future research may be combined with big data research. With the progress of computers, mobile phones, smartwatches, and other smart devices, social media information sources will become more diversified. Future research can combine new sources, such as pictures, videos, and physiological signals, with online social networking to adapt to the development trend of the internet. More medical information talents need to be trained in the future to better solve the problem of network information analysis. Overall, this scoping review can be useful for a large audience that includes researchers entering the field.
社交媒体发展迅速，对其进行有效挖掘和分析已引起医疗保健提供者的广泛关注。据我们所知，大多数综述都集中在社交媒体的应用上，而缺乏整合社交媒体健康信息分析方法的综述。
本范围综述旨在回答以下 4 个问题：（1）研究采用了哪些类型来调查医疗保健社交媒体，（2）采用了哪些方法来分析现有社交媒体上的健康信息，（3）应应用哪些指标来收集和评估医疗保健社交媒体内容分析方法的特征，以及（4）分析医疗保健社交媒体内容的方法目前存在哪些问题和发展方向？
我们按照系统评价和荟萃分析的首选报告项目指南进行了范围综述。我们检索了 PubMed、Web of Science、EMBASE、护理与联合健康文献累积索引以及 Cochrane Library，以获取 2010 年至 2023 年 5 月期间关于社交媒体和医疗保健的原始研究。两名独立审查员根据纳入标准筛选合格研究。对纳入的研究进行叙述性综合。
在 16161 篇确定的引文中有 134 篇（0.8%）研究被纳入本综述。其中包括 67 篇（50.0%）定性设计、43 篇（32.1%）定量设计和 24 篇（17.9%）混合方法设计。应用的研究方法根据以下方面进行分类：（1）手动分析方法（内容分析方法、扎根理论、民族志、分类分析、主题分析和评分表）和计算机辅助分析方法（潜在狄利克雷分配、支持向量机、概率聚类、图像分析、主题建模、情感分析和其他自然语言处理技术），（2）研究内容类别，以及（3）医疗保健领域（医疗实践、医疗服务和健康教育）。
基于广泛的文献综述，我们调查了医疗保健社交媒体内容分析方法，以确定主要应用、差异、趋势和现有问题。我们还讨论了对未来的影响。传统的内容分析仍然是分析社交媒体内容的主流方法，未来的研究可能会结合大数据研究。随着计算机、手机、智能手表等智能设备的进步，社交媒体信息源将变得更加多样化。未来的研究可以结合新的来源，如图片、视频和生理信号，以及在线社交网络，以适应互联网的发展趋势。未来需要培养更多的医学信息人才，以更好地解决网络信息分析问题。总体而言，本范围综述对进入该领域的研究人员等广大受众可能会有所帮助。
