Munmun De Choudhury
Computational Social Science for Better Healthcare
Social networks, Facebook and Twitter are continually creating rich repositories of information relating to activities, emotion and linguistic expression of millions of individuals, around happenings in day-to-day life, whether big or small. By leveraging such trails of data, and developing machine learning models, we can not only elucidate core aspects of human behavior, but can attempt to solve a vista of problems relating to personal health, which have traditionally been challenging.
In this talk I will discuss the harnessing of social media in examining patterns of activity, emotional, and linguistic correlates for childbirth and postnatal course. I will present analyses and computational models that make automated inferences about the status and dynamics of behavioral changes in new mothers following childbirth. Such inferences can eventually help develop unobtrusive diagnostic measures of behavioral disorders in new mothers, such as postpartum depression. Broadly, I will conclude with how this line of research bears potential in enabling better healthcare for the society — particularly in informing the design of new early-warning systems and interventions, to help individuals track mental and behavioral health concerns, and thereby improve their quality of life.
Munmun De Choudhury is a postdoctoral researcher at Microsoft Research, Redmond. Her research interests are in computational social science. By combining machine learning, human computer interaction, and social science, Munmun’s research attempts to decipher human behavior as manifested in people’s online activities. Her honors include: the Grace Hopper Scholarship, finalist of Facebook Fellowship, and winner of Best Paper Honorable Mention award at CHI. Her work has also found its way to general audiences through popular press (e.g., Wall Street Journal, Tech Review); at the same time through technology transfer to product groups at Microsoft (Bing). Earlier, Munmun was a research fellow at Rutgers University, and obtained a PhD in Computer Science from Arizona State University in 2011.