@文章{信息:doi/10.2196/13076，作者="Melvin, Sara and Jamal, Amanda and Hill, Kaitlyn and Wang, Wei and Young, Sean D"，标题="识别睡眠不足的推文作者:前瞻性研究"，期刊="JMIR Ment Health"，年="2019"，月="12"，日="6"，卷="6"，数="12"，页="e13076"，关键词="可穿戴电子设备;安全;自然语言处理;信息存储与检索;睡眠不足;神经网络(计算机);睡眠;背景:社交媒体数据可以作为检测睡眠剥夺的工具。第一学期的一年级本科生被邀请佩戴睡眠追踪设备(基础;英特尔)，让我们在Twitter上关注他们，并完成关于他们睡眠的每周调查。 Objective: This study aimed to determine whether social media data can be used to monitor sleep deprivation. Methods: The sleep data obtained from the device were utilized to create a tiredness model that aided in labeling the tweets as sleep deprived or not at the time of posting. Labeled data were used to train and test a gated recurrent unit (GRU) neural network as to whether or not study participants were sleep deprived at the time of posting. Results: Results from the GRU neural network suggest that it is possible to classify the sleep-deprivation status of a tweet's author with an average area under the curve of 0.68. Conclusions: It is feasible to use social media to identify students' sleep deprivation. The results add to the body of research suggesting that social media data should be further explored as a potential source for monitoring health. ", issn="2368-7959", doi="10.2196/13076", url="https://mental.www.mybigtv.com/2019/12/e13076", url="https://doi.org/10.2196/13076", url="http://www.ncbi.nlm.nih.gov/pubmed/31808747" }