Social Media as a Research Tool (SMaaRT) for Risky Behavior Analytics: Methodological Review.
Social Media as a Research Tool (SMaaRT) for Risky Behavior Analytics: Methodological Review.
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DOI：
10.2196/21660
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发表时间：
2020-11-30
影响因子：
8.5
通讯作者：
Myneni S
中科院分区：
文献类型：
--
作者：
Singh T;Roberts K;Cohen T;Cobb N;Wang J;Fujimoto K;Myneni S
可改变的危险健康行为，如吸烟、过度饮酒、超重、缺乏体育活动和不健康的饮食习惯，是发展慢性健康状况的一些主要因素。社交媒体平台已成为数字时代不可或缺的通信手段。他们提供了一个机会，让个人表达自己，以及分享他们的健康相关的关注与同行和医疗保健提供者，关于危险行为。这种同伴互动可以作为有价值的数据源，以更好地了解人际和自我心理社会中介和社会影响的机制，驱动行为改变。本综述的目的是总结计算和定量技术，以促进通过社交媒体平台上的危险健康行为的同伴互动产生的数据分析。我们于2020年9月通过使用相关关键词（如“社交媒体”、“在线健康社区”、“机器学习”、“数据挖掘”等）检索三个数据库（PubMed、Web of Science和Scopus）对文献进行了系统性综述。研究报告遵循PRISMA（系统性综述和荟萃分析首选报告项目）指南。两名评审员根据纳入和排除标准独立评估研究的合格性。我们从选定的研究中提取了所需的信息。初始检索共返回1554项研究，在仔细分析标题、摘要和全文后，共有64项研究纳入本综述。我们从所有研究中提取了以下关键特征：用于进行研究的社交媒体平台，研究的危险健康行为，分析的帖子数量，研究重点，用于数据分析的关键方法功能和工具，使用的评估指标以及关键发现的总结。最常用的社交媒体平台是Twitter，其次是Facebook、QuitNet和Reddit。最常被研究的危险健康行为是尼古丁使用，其次是药物或物质滥用和酒精使用。各种监督和无监督机器学习方法被用于分析从在线对等交互生成的文本数据。很少有研究使用深度学习方法来分析文本数据以及图像或视频数据。正如一些研究所报告的那样，还进行了社会网络分析。我们的综述巩固了分析危险健康行为的方法论基础，并增强了我们对如何利用社交媒体进行细致入微的行为建模和表示的理解。从我们的审查中获得的知识可以作为针对个人和群体水平的有说服力的健康传播和有效的行为矫正技术的发展的基础组成部分。
Modifiable risky health behaviors, such as tobacco use, excessive alcohol use, being overweight, lack of physical activity, and unhealthy eating habits, are some of the major factors for developing chronic health conditions. Social media platforms have become indispensable means of communication in the digital era. They provide an opportunity for individuals to express themselves, as well as share their health-related concerns with peers and health care providers, with respect to risky behaviors. Such peer interactions can be utilized as valuable data sources to better understand inter-and intrapersonal psychosocial mediators and the mechanisms of social influence that drive behavior change. The objective of this review is to summarize computational and quantitative techniques facilitating the analysis of data generated through peer interactions pertaining to risky health behaviors on social media platforms. We performed a systematic review of the literature in September 2020 by searching three databases—PubMed, Web of Science, and Scopus—using relevant keywords, such as “social media,” “online health communities,” “machine learning,” “data mining,” etc. The reporting of the studies was directed by the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. Two reviewers independently assessed the eligibility of studies based on the inclusion and exclusion criteria. We extracted the required information from the selected studies. The initial search returned a total of 1554 studies, and after careful analysis of titles, abstracts, and full texts, a total of 64 studies were included in this review. We extracted the following key characteristics from all of the studies: social media platform used for conducting the study, risky health behavior studied, the number of posts analyzed, study focus, key methodological functions and tools used for data analysis, evaluation metrics used, and summary of the key findings. The most commonly used social media platform was Twitter, followed by Facebook, QuitNet, and Reddit. The most commonly studied risky health behavior was nicotine use, followed by drug or substance abuse and alcohol use. Various supervised and unsupervised machine learning approaches were used for analyzing textual data generated from online peer interactions. Few studies utilized deep learning methods for analyzing textual data as well as image or video data. Social network analysis was also performed, as reported in some studies. Our review consolidates the methodological underpinnings for analyzing risky health behaviors and has enhanced our understanding of how social media can be leveraged for nuanced behavioral modeling and representation. The knowledge gained from our review can serve as a foundational component for the development of persuasive health communication and effective behavior modification technologies aimed at the individual and population levels.