杂志文章%@ 2369-2960 %I JMIR出版物%V 6 %N 卡塔尔世界杯8强波胆分析4 %P e21660 %T社交媒体作为风险行为分析的研究工具(smarart):%A Singh,Tavleen %A Roberts,Kirk %A Cohen,Trevor %A Cobb,Nathan %A Wang,Jing %A Fujimoto,Kayo %A Myneni,Sahiti %+生物医学信息学院，德克萨斯大学健康科学中心，7000 Fannin Street, Suite 600，德克萨斯州休斯敦，77030,1 713 500 3900，tavleen.kaur.ranjit.singh@uth.tmc.edu %K社交媒体%K信息流行病学%K信息监测%K在线健康社区%K风险健康行为%K数据挖掘%K机器学习%K自然语言处理%K文本挖掘%D 2020 %7 30.11.2020 %9审查%J JMIR公共卫生监测%G英语%X背景: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，是发展成慢性健康疾病的一些主要因素。社交媒体平台已经成为数字时代不可或缺的交流手段。它们为个人提供了一个机会，让他们表达自己的看法，并与同龄人和卫生保健提供者分享他们对危险行为的健康方面的关切。这种同伴间的互动可以作为有价值的数据来源，以更好地理解个人之间和个人内部的社会心理媒介以及驱动行为改变的社会影响机制。目的:本综述的目的是总结计算和定量技术，以促进分析通过社交媒体平台上与风险健康行为相关的同伴交互产生的数据。方法:我们在2020年9月通过搜索pubmed、Web of Science和scopus三个数据库，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，对文献进行了系统回顾。研究报告遵循PRISMA(系统回顾和荟萃分析首选报告项目)指南。 Two reviewers independently assessed the eligibility of studies based on the inclusion and exclusion criteria. We extracted the required information from the selected studies. Results: 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. Conclusions: 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. %M 33252345 %R 10.2196/21660 %U http://publichealth.www.mybigtv.com/2020/4/e21660/ %U https://doi.org/10.2196/21660 %U http://www.ncbi.nlm.nih.gov/pubmed/33252345