@Article{info:doi/10.2196/13076,
author="Melvin, Sara
and Jamal, Amanda
and Hill, Kaitlyn
and Wang, Wei
and Young, Sean D",
title="Identifying Sleep-Deprived Authors of Tweets: Prospective Study",
journal="JMIR Ment Health",
year="2019",
month="Dec",
day="6",
volume="6",
number="12",
pages="e13076",
keywords="wearable electronic devices; safety; natural language processing; information storage and retrieval; sleep deprivation; neural networks (computer); sleep; social media",
abstract="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. ",
issn="2368-7959",
doi="10.2196/13076",
url="https://mental.jmir.org/2019/12/e13076",
url="https://doi.org/10.2196/13076",
url="http://www.ncbi.nlm.nih.gov/pubmed/31808747"
}