%0 Journal Article
%@ 2368-7959
%I JMIR Publications
%V 6
%N 12
%P e13076
%T Identifying Sleep-Deprived Authors of Tweets: Prospective Study
%A Melvin,Sara
%A Jamal,Amanda
%A Hill,Kaitlyn
%A Wang,Wei
%A Young,Sean D
%+ Department of Medicine, University of California, Irvine, 333 City Blvd West, Suite 640, Orange, CA, United States, 1 310 456 5239, syoung5@uci.edu
%K wearable electronic devices
%K safety
%K natural language processing
%K information storage and retrieval
%K sleep deprivation
%K neural networks (computer)
%K sleep
%K social media
%D 2019
%7 6.12.2019
%9 Original Paper
%J JMIR Ment Health
%G English
%X Background: Social media data can be explored as a tool to detect sleep deprivation. First-year undergraduate students in their first quarter were invited to wear sleep-tracking devices (Basis; Intel), allow us to follow them on Twitter, and complete weekly surveys regarding their sleep. 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. 
%M 31808747
%R 10.2196/13076
%U https://mental.jmir.org/2019/12/e13076
%U https://doi.org/10.2196/13076
%U http://www.ncbi.nlm.nih.gov/pubmed/31808747