%0期刊文章%@ 1438- 8871% I JMIR出版物%V 20卡塔尔世界杯8强波胆分析% N 9% P e11652 %T使用部分观察的Facebook网络开发基于同伴的艾滋病毒预防干预:案例研究%A Khanna,Aditya Subhash %A Goodreau,Steven Michael %A Michaels,Stuart Schneider,John Alexis %+芝加哥大学医学院芝加哥艾滋病毒消除中心，芝加哥，IL，芝加哥，MC 5065, 5841 S Maryland Avenue，邮编:1773 834 5635，邮编:60637，邮编:1773 834 5635，akhanna@medicine.bsd.uchicago.edu %K非裔美国人%K计算机模拟%K数据挖掘%K HIV感染%K同龄群体%K暴露前预防%K性和性别少数群体%K社交媒体%K社交网络%D 2018 %7 14.09.2018 %9原始论文%J J医学互联网Res %G英语%X背景:这是一个来自与男性发生性关系的年轻黑人男性的HIV预防项目的案例研究。个人层面的预防干预措施在男男性行为的年轻黑人男性中收效有限，这一人群受艾滋病毒的影响尤为严重;基于同伴网络的干预是一个很有前途的选择。Facebook是一个有吸引力的数字平台，因为它可以对社交网络进行广泛的描述。然而，在使用Facebook数据进行同伴干预方面存在一些挑战，包括Facebook网络的庞大规模，难以评估确定候选同伴变化代理的适当方法，边界规范问题，以及对社交网络数据的局部观察。目的:本研究旨在探索使用社交Facebook网络来设计基于同伴网络的艾滋病毒预防干预措施的方法挑战，并提出克服这些挑战的技术。方法:我们的样本包括298名uConnect研究的受访者，他们亲自回答了一项生物行为调查，并下载了他们的Facebook好友列表(2013-2014年)。这项研究的参与者在Facebook上有超过18万的朋友，他们没有参与这项研究(非受访者)。 We did not observe friendships between these nonrespondents. Given the large number of nonrespondents whose networks were partially observed, a relational boundary was specified to select nonrespondents who were well connected to the study respondents and who may be more likely to influence the health behaviors of young black men who have sex with men. A stochastic model-based imputation technique, derived from the exponential random graph models, was applied to simulate 100 networks where unobserved friendships between nonrespondents were imputed. To identify peer change agents, the eigenvector centrality and keyplayer positive algorithms were used; both algorithms are suitable for identifying individuals in key network positions for information diffusion. For both algorithms, we assessed the sensitivity of identified peer change agents to the imputation model, the stability of identified peer change agents across the imputed networks, and the effect of the boundary specification on the identification of peer change agents. Results: All respondents and 78.9% (183/232) of nonrespondents selected as peer change agents by eigenvector on the imputed networks were also selected as peer change agents on the observed networks. For keyplayer, the agreement was much lower; 42.7% (47/110) and 35.3% (110/312) of respondent and nonrespondent peer change agents, respectively, selected on the imputed networks were also selected on the observed network. Eigenvector also produced a stable set of peer change agents across the 100 imputed networks and was much less sensitive to the specified relational boundary. Conclusions: Although we do not have a gold standard indicating which algorithm produces the most optimal set of peer change agents, the lower sensitivity of eigenvector centrality to key assumptions leads us to conclude that it may be preferable. The methods we employed to address the challenges in using Facebook networks may prove timely, given the rapidly increasing interest in using online social networks to improve population health. %M 30217793 %R 10.2196/11652 %U //www.mybigtv.com/2018/9/e11652/ %U https://doi.org/10.2196/11652 %U http://www.ncbi.nlm.nih.gov/pubmed/30217793