TY  - JOUR
AU  - Melvin, Sara
AU  - Jamal, Amanda
AU  - Hill, Kaitlyn
AU  - Wang, Wei
AU  - Young, Sean D
PY  - 2019
DA  - 2019/12/6
TI  - Identifying Sleep-Deprived Authors of Tweets: Prospective Study
JO  - JMIR Ment Health
SP  - e13076
VL  - 6
IS  - 12
KW  - wearable electronic devices
KW  - safety
KW  - natural language processing
KW  - information storage and retrieval
KW  - sleep deprivation
KW  - neural networks (computer)
KW  - sleep
KW  - social media
AB  - 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. 
SN  - 2368-7959
UR  - https://mental.jmir.org/2019/12/e13076
UR  - https://doi.org/10.2196/13076
UR  - http://www.ncbi.nlm.nih.gov/pubmed/31808747
DO  - 10.2196/13076
ID  - info:doi/10.2196/13076
ER  -