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.
Keywords: information storage and retrieval; natural language processing; neural networks (computer); safety; sleep; sleep deprivation; social media; wearable electronic devices.
©Sara Melvin, Amanda Jamal, Kaitlyn Hill, Wei Wang, Sean D Young. Originally published in JMIR Mental Health (http://mental.jmir.org), 06.12.2019.