Johannes C. Eichstaedt, Senior Research Associate, Positive Psychology Center, University of Pennsylvania
Title: Measuring Physical and Mental Health Using Social Media
Abstract: The content shared on social media is among the largest data sets on human behavior in history. In my work, I seek to leverage this data to address questions in the psychological sciences. Specifically, I apply natural language processing and machine learning to characterize and measure psychological phenomena with a focus on mental and physical health. For depression, I will show that machine learning models applied to Facebook status histories can predict future depression as documented in the medical records of a sample of patients. For heart disease, the leading cause of death, I demonstrate how prediction models derived from geo-tagged Tweets can estimate county mortality rates better than gold-standard epidemiological models. I will also present preliminary findings on my emerging project to measure the subjective well-being of large populations. Across these studies, I argue that AI-based approaches to social media can augment clinical practice, guide prevention, and inform public policy.
Johannes C. Eichstaedt is a computational social scientist at the University of Pennsylvania, where he obtained his Ph.D. in psychology in 2017 and has since been a Postdoc and Senior Research Associate. In 2011 he co-founded and led the World Well-Being Project, which is a lab comprised of psychologists and computer scientists focused on measuring the psychological states of populations using social media and machine learning. Johannes has also received an M.S. in particle physics from the University of Chicago, and two Masters in Psychology from the University of Pennsylvania. In 2014, he was elected an Emerging Leader in Science & Society by the American Association for Advancement of Science (AAAS).