Mining Social Media to Improve Public Health

People posting to social media on smartphones can be viewed as an organic sensor network for public health data, picking up information about the spread of disease, lifestyle factors that influence health, and pinpointing sources of disease. We show how a faint but actionable signal can be detected in vast amounts of social media data using statistical natural language and social network models. We present case studies of predicting influenza transmission and per-city rates, discovering patterns of alcohol consumption in different neighborhoods, and tracking down the sources of foodborne illness. 

This lecture satisfies requirements for CSCI 591: Research Colloquium