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Modeling Smart Wristband Sleep Data to Classify Undergraduates’ Sleep Deprived Tweets
Sara Melvin;
Amanda Jamal;
Kaitlyn Hill;
Wei Wang;
Sean Young
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:
To determine whether social media data can be used to monitor sleep deprivation.
Methods:
The device sleep data were used to label the tweets as sleep deprived or not at the time of Twitter post. These labeled data were used to train and test a Gated Recurrent Unit (GRU) neural network as to whether the participants were sleep deprived at the time of the post or not.
Results:
Results from a GRU neural network suggest it is possible to classify the sleep deprivation status of a tweet’s author with an average area under the curve (AUC) of 0.68.
Conclusions:
It is feasible to use social media data to identify students’ sleep deprivation. Results add to the body of research suggesting that social media data should be further explored for a potential source for monitoring mental and behavioral health. Clinical Trial: N/A
Citation
Please cite as:
Melvin S, Jamal A, Hill K, Wang W, Young S
Identifying Sleep-Deprived Authors of Tweets: Prospective Study
