How Machine Learning Builds Meaning from Our Chats, Tweets, and Likes
Emerj AI Research
March 06, 2016
April 08, 2026
An in-depth interview covering Penn research analyzing social media language to understand personality, well-being, and health — from predicting heart disease via angry tweets to potential early warnings for addiction relapse. Ungar discusses the practical applications of the “quantified self” and how community-level social media insights could inform public policy. Featured: Lyle Ungar.