Twitter May Prove Valuable in Uncovering Mental Illness Trends: Study

By reviewing tweets from users who publicly mentioned their diagnosis and by looking for language cues linked to certain disorders, researchers have been able to quickly and inexpensively collect new data on post-traumatic stress disorder, depression, bipolar disorder and seasonal affective disorder.

The researchers at Johns Hopkins University in US said their goal is to share with treatment providers and public health officials some timely additional information about the prevalence of certain mental illnesses.

Using computer technology to sift through tweets, they said, can help address the slow pace and high costs associated with collecting mental heath data through surveys and other traditional methods.

"With many physical illnesses, including the flu, there are lots of quantifiable facts and figures that can be used to study things like how often and where the disease is occurring, which people are most vulnerable and what treatments are most successful," said Glen Coppersmith, a Johns Hopkins senior research scientist.

"We're not aiming to replace the long-standing survey methods of tracking mental illness trends. We believe our new techniques could complement that process.

"We're trying to show that analysing tweets could uncover similar results, but could do so more quickly and at a much lower cost," Coppersmith said.

The analyses indicated that PTSD was more prevalent at military installations that frequently deployed during the recent Iraq and Afghanistan conflicts, and that signs of depression were more evident in locations with higher unemployment rates, researchers said.

They demonstrate that analysing Twitter posts could become a useful yardstick in quickly measuring mental health trends, particularly after dramatic events such as natural disasters and military conflicts.

The formula for zeroing in on mental health cases was based on a review of more than 8 billion tweets.

"Using Twitter to get a fix on mental health cases could be very helpful to health practitioners and governmental officials who need to decide where counselling and other care is needed most," said Mark Dredze, an assistant research professor in the Whiting School of Engineering's Department of Computer Science.