Reading Between the Tweets: Social Technologies for Predicting and Changing Health Behavior

Event Description

Social technologies and their associated data are increasingly being used as tools in public health research and practice. Examples include social media, mobile apps, internet searches, and wearable sensors. More than half the world (4.5 billion people) uses social media sites to create, share, and discuss content—often in the form of personal thoughts, behaviors, and clinical diagnoses. Dr. Young will discuss how social technologies and data (e.g., artificial intelligence and data science modeling) are being used to impact public health and how researchers and health departments/agencies might apply them in public health surveillance/intervention efforts. He will also present his team’s research on how these tools can be employed to predict and change health behaviors, and on implementation-related issues such as policy and ethical questions. The studies to be discussed involve populations affected by HIV, mental health and substance use disorders, car crashes, or COVID-19.

Biosketch

Sean D. Young, Ph.D., M.S., is executive director of the University of California Institute for Prediction Technology (UCIPT) and associate professor (a split appointment) in the Department of Emergency Medicine, School of Medicine, and the Department of Informatics, Donald Bren School of Information and Computer Sciences, at the University of California, Irvine. UCIPT brings together researchers across University of California campuses from a wide variety of disciplines to study how social “big data,” machine learning, and data mining can be used to predict real-world events. Dr. Young is also the author of a bestselling book, Stick With It: A Scientifically Proven Process for Changing Your Life—for Good. He is a member of the Board on Population Health and Public Health Practice within the National Academies of Science, Engineering, and Medicine.

Dr. Young’s research focuses on: (1) using social big data to monitor and predict public health issues such as HIV, substance use, suicide, and public safety/crime; and (2) designing and testing technologies to address public health and medical issues among at-risk populations such as African Americans, Latinxs, or men who have sex with men (MSM). He earned his doctorate in psychology and his master’s degrees in psychology and in health services research from Stanford University. NIH funders of his research include NCCIH and the National Institute of Neurological Disorders and Stroke (through the NIH HEAL [Helping to End Addiction Long-term^SM] Initiative), as well as the National Institute on Drug Abuse, National Center for Advancing Translational Sciences, National Institute of Allergy and Infectious Diseases, and National Institute of Mental Health. Dr. Young received a Ruth L. Kirschstein National Research Service Award and a T32 fellowship.

Learning Objectives:

1. Learn how artificial intelligence modeling on social data might be integrated into public health surveillance efforts.
2. Describe the Harnessing Online Peer Education (HOPE) online community intervention and its acceptability and effectiveness as a method for changing health behaviors among racial, ethnic, and sexual minorities. 
3. Give examples of potential implementation and policy issues related to use of technologies and technology data in public health surveillance and interventions.*