@Article{信息:doi 10.2196 / / jmir。1985，作者=“Fernandez-Luque, Luis and Karlsen, Randi and Melton, Genevieve B”，标题=“HealthTrust:一种检索在线健康视频的社交网络方法”，期刊=“J Med Internet Res”，年=“2012”，月=“Jan”，日=“31”，卷=“14”，数=“1”，页=“e22”，关键词=“医学信息学;信息存储与检索;视频;在线系统;健康的沟通;背景:社交媒体正在成为健康领域的主流。尽管社交媒体平台上有大量准确可靠的健康信息，但要找到高质量的健康信息却很困难。误导性的健康信息通常很受欢迎(例如，反疫苗接种视频)，因此在一般搜索引擎上得到了很高的评价。我们相信，可以利用社区关于卫生信息质量的智慧来帮助创建检索高质量社交媒体内容的工具。 Objectives: To explore approaches for extracting metrics about authoritativeness in online health communities and how these metrics positively correlate with the quality of the content. Methods: We designed a metric, called HealthTrust, that estimates the trustworthiness of social media content (eg, blog posts or videos) in a health community. The HealthTrust metric calculates reputation in an online health community based on link analysis. We used the metric to retrieve YouTube videos and channels about diabetes. In two different experiments, health consumers provided 427 ratings of 17 videos and professionals gave 162 ratings of 23 videos. In addition, two professionals reviewed 30 diabetes channels. Results: HealthTrust may be used for retrieving online videos on diabetes, since it performed better than YouTube Search in most cases. Overall, of 20 potential channels, HealthTrust's filtering allowed only 3 bad channels (15{\%}) versus 8 (40{\%}) on the YouTube list. Misleading and graphic videos (eg, featuring amputations) were more commonly found by YouTube Search than by searches based on HealthTrust. However, some videos from trusted sources had low HealthTrust scores, mostly from general health content providers, and therefore not highly connected in the diabetes community. When comparing video ratings from our reviewers, we found that HealthTrust achieved a positive and statistically significant correlation with professionals (Pearson r10 = .65, P = .02) and a trend toward significance with health consumers (r7 = .65, P = .06) with videos on hemoglobinA1c, but it did not perform as well with diabetic foot videos. Conclusions: The trust-based metric HealthTrust showed promising results when used to retrieve diabetes content from YouTube. Our research indicates that social network analysis may be used to identify trustworthy social media in health communities. ", issn="1438-8871", doi="10.2196/jmir.1985", url="//www.mybigtv.com/2012/1/e22/", url="https://doi.org/10.2196/jmir.1985", url="http://www.ncbi.nlm.nih.gov/pubmed/22356723" }