Social Media data as a tool for computational psychiatry research

There is growing interest in understanding the evolution of depressive symptomatology over time, the dynamics of how symptoms interact during periods of wellness and as one approaches an episode of illness. It is thought that by understanding these dynamics we can develop tools to identify early warning signs of depression before it takes hold. But this sort of research is prohibitively challenging; it requires research participants to actively log their thoughts, feelings, and emotions regularly, over months or even years to capture critical transitions into a depressed state. An alternative is to use sources of data, such as social media posts, that people produce routinely in the course of their everyday life. Recent data has shown this might be possible; depressed individuals use language differently, for example, using more first-person singular pronouns (I, me, my) and more emotional negative words (hurt, ugly, nasty). In a set of two studies, I will present research testing if we can use social media posts to detect depression, I will test how specific such findings are to depression versus other aspects of mental health and finally, if these ‘linguistic symptoms’ can be used to test core theories about the network structure of depression and how it changes during episodes of illness.