The team also noticed that certain isolated words in Twitter posts also were characteristic of depression. Words like anxiety, severe, appetite, suicidal, nausea, drowsiness, fatigue, nervousness, addictive, attacks, episodes, and sleep were used by depressed users, but more surprisingly, words like she, him, girl, game, men, home, fun, house, favorite, wants, tolerance, cope, amazing, love, care, songs, and movie could be indications of depression as well.

The volume of tweets mattered too, as did the percentage of exchanges—users who are depressed begin to tweet less, and tweet less at other people, indicating a possible loss of social connectedness, said Horvitz. Of course, just because a Twitter user makes a post that includes the word fatigue and house at 4am, that doesn’t mean they’re depressed. The Microsoft team’s classifier looked at users’ feeds over long periods of time and incorporated many factors. A second Microsoft study that focused more on broader populations using slightly different methods achieved similar results, determining depression in tweets with around 70% accuracy.

One area of public health where this kind of research could come in handy is in measuring public reactions to events. Tracking public Twitter feeds after profound or traumatic events could help scientists understand how we’re affected by the news. “We really didn’t used to have many tools available traditionally for that kind of fine-grained analysis,” says said Horvitz. “Now there’s a new direction for doing the science.”