@文章{info:doi/ 10.21960 /21660，作者=“Singh, Tavleen和Roberts, Kirk和Cohen, Trevor和Cobb, Nathan和Wang, Jing和Fujimoto, Kayo和Myneni, Sahiti”，标题=“社交媒体作为风险行为分析的研究工具(smart):方法论综述”，期刊=“JMIR公共健康监测”，年=“2020”，月=“11月”，日=“30”，卷=“6”，数=“4”，页=“e21660”，关键词=“社交媒体;infodemiology;infoveillance;在线健康社区的做法;危险的健康行为;数据挖掘;机器学习;自然语言处理;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。 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. Objective: 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. Methods: 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. 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. ", issn="2369-2960", doi="10.2196/21660", url="http://publichealth.www.mybigtv.com/2020/4/e21660/", url="https://doi.org/10.2196/21660", url="http://www.ncbi.nlm.nih.gov/pubmed/33252345" }