ASSET Seminar: Computational Social Listening for Public Health (Sharath Guntuku, University of Pennsylvania)

Presentation Abstract:

How can A.I.-based methods inform social listening applications during public health crises? The COVID-19 pandemic has uprooted the mode and method of human communication and interaction. The magnitude of the pandemic has led to an ‘infodemic’ along with considerable increase in stress and anxiety across communities. At the same time, the use of social media has increased dramatically as individuals sheltered in place. In this talk, I will introduce how our group is using big data from social media sources for contributing to social good. I will discuss the application of machine learning and natural language processing techniques to obtain insights on the heterogeneity in vaccine acceptance and mental health measurement across communities in the United States and outside.

Speaker Bio:

Sharath Guntuku is an Assistant Professor in the research-track in the Department of Computer and Information Science at the University of Pennsylvania and a Senior Fellow at the Leonard Davis Institute of Health Economics. His research focuses on building predictive models for and uncovering insight into health outcomes and psychological states of individuals and communities. The goal of this research is to supplement clinical diagnoses and facilitate early and personalized interventions for improving treatments and well-being. His team develops computational models utilizing large-scale user-generated text, image, and mobile sensor data to answer questions pertaining to geospatial, cross-cultural, and temporal aspects of human behavior. His research is supported by the National Institutes of Health, Penn Global, and the World Bank Group and has been covered by the American Psychological Association, WIRED, Canadian Broadcasting Company, The Atlantic, US News, and other venues.