@Article{info:doi/10.2196/35974，作者=“Khademi Habibabadi, Sedigheh and Hallinan, Christine and Bonomo, Yvonne and Conway, Mike”，标题=“消费者生成的大麻作为药物的话语:技术范围审查”，期刊=“J Med Internet Res”，年=“2022”，月=“11”，日=“16”，卷=“24”，数=“11”，页=“e35974”，关键词=“社交媒体;数据挖掘;互联网和网络技术;消费者产生数据;药用大麻;背景:药用大麻越来越多地被用于各种身心健康状况。社交媒体和基于网络的健康平台提供了宝贵的、实时的和具有成本效益的监测资源，用于收集有关将大麻用于医疗目的的个人的见解。考虑到医用大麻最佳使用的证据仍在不断出现，这一点尤其重要。尽管在网上向消费者销售医用大麻，但目前没有强有力的监管框架来衡量临床健康益处或个人不良事件经历。在之前的一项研究中，我们对包含大麻药用主题的研究进行了系统的范围审查，并使用了来自社交媒体和搜索引擎结果的数据。 This study analyzed the methodological approaches and limitations of these studies. Objective: We aimed to examine research approaches and study methodologies that use web-based user-generated text to study the use of cannabis as a medicine. Methods: We searched MEDLINE, Scopus, Web of Science, and Embase databases for primary studies in the English language from January 1974 to April 2022. Studies were included if they aimed to understand web-based user-generated text related to health conditions where cannabis is used as a medicine or where health was mentioned in general cannabis-related conversations. Results: We included 42 articles in this review. In these articles, Twitter was used 3 times more than other computer-generated sources, including Reddit, web-based forums, GoFundMe, YouTube, and Google Trends. Analytical methods included sentiment assessment, thematic analysis (manual and automatic), social network analysis, and geographic analysis. Conclusions: This study is the first to review techniques used by research on consumer-generated text for understanding cannabis as a medicine. It is increasingly evident that consumer-generated data offer opportunities for a greater understanding of individual behavior and population health outcomes. However, research using these data has some limitations that include difficulties in establishing sample representativeness and a lack of methodological best practices. To address these limitations, deidentified annotated data sources should be made publicly available, researchers should determine the origins of posts (organizations, bots, power users, or ordinary individuals), and powerful analytical techniques should be used. ", issn="1438-8871", doi="10.2196/35974", url="//www.mybigtv.com/2022/11/e35974", url="https://doi.org/10.2196/35974", url="http://www.ncbi.nlm.nih.gov/pubmed/3638" }