Measuring Physical and Mental Health Using Social Media

Johannes Eichstaedt

(UPENN)


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Date: June 10, 2019

Description:

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.




Created: Monday, June 10th, 2019