Dr. Lyle Ungar will be chatting with #SPSM, 9pCT 4/17/16, about automated ways to analyze the content in social media data, and how this can be applied to the field of suicide prevention. You can read more about his work here and here and here.
What if we can automatically detect signals in social media content to learn about population mental health and suicide risk without waiting years for suicide mortality data collected in traditional ways? What if we could detect information about what drives changes in population mental health and suicide risk? Ungar can tell us *how* this is possible today. The real question is what is stopping us from doing this on a broad scale, and what will we do with that information.
Watch his TEDx here:
You can watch our chat streaming LIVE at 9pCT here:
Dr. Lyle Ungar is a Professor of Computer and Information Science at
the University of Pennsylvania, where he also holds appointments in
multiple departments in the Schools of Business, Medicine, Arts and
Sciences, and Engineering and Applied Science. Lyle received a
B.S. from Stanford University and a Ph.D. from M.I.T. He has
published over 200 articles and in co-inventor on eleven patents. His
current research focuses on developing scalable machine learning
methods for data mining and text mining, including spectral methods
for NLP, and analysis of social media to better understand the drivers
of physical and mental well-being.