@Article{信息:doi 10.2196 / / jmir。2514，作者="Bobicev, Victoria和Sokolova, Marina和El Emam, Khaled和Jafer, Yasser和Dewar, Brian和Jonker, Elizabeth和Matwin, Stan"，标题="能否重新识别医学论坛上的匿名海报?"，期刊="J Med Internet Res"，年="2013"，月="10"，日="03"，卷="15"，号="10"，页="e215"，关键词="隐私;个人健康信息;医学论坛;背景:医学论坛的参与者经常在他们的在线帖子中透露自己的个人健康信息。为了不介意披露敏感的个人健康信息，一些参与者可能会通过匿名发帖来隐藏自己的身份。他们可以通过使用假身份、昵称或笔名来做到这一点，而这些身份无法轻易被追踪到。然而，每个人的写作风格都有其独特的特点，通过作者归因分析可以确定匿名用户的真实身份。尽管之前有关于作者身份归因问题的研究，但在医学论坛上关于作者身份自动归因的研究一直缺乏。本文的重点是证明在医学论坛中基于字符的作者归因比基于文字的方法工作得更好。 Objective: The goal was to build a system that accurately attributes authorship of messages posted on medical forums. The Authorship Attributor system uses text analysis techniques to crawl medical forums and automatically correlate messages written by the same authors. Authorship Attributor processes unstructured texts regardless of the document type, context, and content. Methods: The messages were labeled by nicknames of the forum participants. We evaluated the system's performance through its accuracy on 6000 messages gathered from 2 medical forums on an in vitro fertilization (IVF) support website. Results: Given 2 lists of candidate authors (30 and 50 candidates, respectively), we obtained an F score accuracy in detecting authors of 75{\%} to 80{\%} on messages containing 100 to 150 words on average, and 97.9{\%} on longer messages containing at least 300 words. Conclusions: Authorship can be successfully detected in short free-form messages posted on medical forums. This raises a concern about the meaningfulness of anonymous posting on such medical forums. Authorship attribution tools can be used to warn consumers wishing to post anonymously about the likelihood of their identity being determined. ", issn="14388871", doi="10.2196/jmir.2514", url="//www.mybigtv.com/2013/10/e215/", url="https://doi.org/10.2196/jmir.2514", url="http://www.ncbi.nlm.nih.gov/pubmed/24091380" }