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.

Bio:
Henry Kautz is the Robin & Tim Wentworth Director of the Goergen Institute for Data Science and Professor in the Department of Computer Science at the University of Rochester. He has served as department head at AT&T Bell Labs in Murray Hill, NJ, and as a full professor at the University of Washington, Seattle. In 2010 he was elected President of the Association for Advancement of Artificial Intelligence, and in 2016 was elected Chair of the AAAS Section on Information, Computing, and Communication. His research in artificial intelligence, pervasive computing, and healthcare applications has led him to be honored as a Fellow of the American Association for the Advancement of Science, Fellow of the Association for Computing Machinery, and Fellow of the AAAI.