Scientists have developed a “digital phenotype,” or a baseline profile of what a person suffering from insomnia or other sleep disorders “looks” like on Twitter.
The study is one of the first to look at relationships between social media use and sleep issues, and based on assessments of the sentiments expressed in users’ tweets gives preliminary hints that patients with sleep disorders may be at a greater risk of psychosocial issues.
Historically, population-level research on sleep disorders in the US has relied on survey methods such as the Behavior Risk Factor Surveillance System.
However, such methods are time- and resource intensive, expensive, suffer from long lag times before reporting and are not generalisable to the larger US population.
Research based on social media data may help overcome these limitations, according to researchers from Boston Children’s Hospital and Merck.
“We wanted to see if we could use new forms of online data, such as Twitter, to characterise the sleep disordered individual and possibly uncover new, previously-undescribed populations of patients suffering sleep problems,” said John Brownstein who directs the hospital’s Computational Epidemiology Group.
The research team used publically available anonymised data from Twitter to create a virtual cohort of 896 active Twitter users whose tweets contained sleep-related words (eg, “can’t sleep,” “insomnia”), or hashtags (eg, #cantsleep, #teamnosleep), or the names of common sleep aids or medications.
They then compared data from that cohort to those of a second group of 934 users who did not tweet using sleep-related terms.
The team examined each user’s age, total number of tweets, total numbers of followers or people followed, number of favourite tweets (that is, the number of tweets by others that the user had favourite), length of time on Twitter (that is, how long the user had had an active Twitter account), average number of tweets per day, location and time zone.
The researchers then figured out what a profile of a Twitter user with sleep issues – compared to a Twitter user without – looked like.
A Twitter user with sleep issues is likely to have been active on Twitter for a relatively long time; has fewer followers and follows fewer people; posts few tweets per day on average; more active on Twitter between 6:00 pm and 5:59 am; more active on Twitter on weekends and early weekdays; and more likely to post tweets with negative sentiment.
“Taken together, the data suggest that Twitter users suffering from a sleep disorder are less active on Twitter on average but tweet more during traditional sleeping hours. The increase in negative sentiment in their tweets suggests that sleep-disordered users could be at an increased risk for psychosocial issues,” researchers said.