The language people use on social media provides a rich, if imperfect portal into who they are, revealing psychological traits and drivers of mental and physical well-being. We show how models built on tens of millions of Facebook posts can help diagnose and understand depression and how the language in billions of tweets compared to county-level health data suggest potential psychological risk factors for heart disease.
This lecture will feature Dr. Lyle Ungar, Professor of Computer and Information Science at the University of Pennsylvania. Dr. Ungar also holds appointments in multiple departments in the Schools of Business, Medicine, Arts and Sciences, and Engineering and Applied Science. Dr. Ungar received a B.S. from Stanford University and a Ph.D. from M.I.T. He has published over 300 articles, supervised two dozen Ph.D. students, and is co-inventor on ten patents. His current research focuses on developing scalable machine learning methods for data mining and text mining, including deep learning methods for natural language processing, and analysis of cell phone and social media to better understand the drivers of physical and mental well-being.