Language signal and mental health

The facts and figures regarding the mental health crisis in this country are staggering; to cite just a few numbers, between 1996 and 2011, annual expenditures on mental disorders rose from $35.2B to $113B, some 25 million American adults will have an episode of major depression this year, suicide is the third leading cause of death for people between 10 and 24 years old, and 89.3 million Americans live in federally-designated Mental Health Professional Shortage Areas. There is a new, extremely promising line of research that is on the upswing that may help address these issues: using signal in people's language, e.g. what they say on social media, as a source of evidence for early detection and/or monitoring. 

Recognizing this as an area where the R&D community is starting to see serious activity, Philip Resnik of the Department of Linguistics and the Institute for Advanced Computer Studies (working with Rebecca Resnik, a clinical psychologist, and Microsoft researcher Meg Mitchell) instigated the first-ever Workshop on Computational Linguistics and Clinical Psychology in 2014 (clpsych.wordpress.com), and a follow-on workshop in 2015 (clpsych.org) contributed to the building momentum. At UMD, Professor Resnik and collaborators are applying methods from computational linguistics and machine learning to develop predictive models connecting language use with mental conditions, with a focus on depression, suicidality, and PTSD. 
 
Philip Resnik
Department of Linguistics
Department of Computer Science
University of Maryland Institute for Advanced Computer Studies
 
Insert photo to be supplied by Philip Resnik