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Columbia University’s Data Science Institute Presents:
DATA SCIENCE DAY

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Wednesday, April 6 • 2:00pm - 4:30pm
Using Social Media to Detect Foodborne Disease Outbreaks [P10]

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Foodborne diseases are estimated to cause 3,000 deaths per year in the United States and many of these cases are caused by restaurants. Identifying and acting on outbreaks of these foodborne diseases quickly is essential to mitigating their negative effects on public health. Although there are official channels to report incidents of food poisoning to the local health departments, often restaurant patrons instead post informal complaints about their negative experiences on social media sites, such as Yelp and Twitter. In this work we construct a social media analysis system, targeted at identifying food poisoning-related documents from multiple online noisy text sources. By tracking social media posts from NYC that contain information about food poisoning incidents, we can successfully identify foodborne illness outbreaks that would otherwise go uninvestigated. These actionable inferences are then utilized by the NYC Department of Health to alleviate the consequences of outbreaks in NYC restaurants more effectively.

Demo/Poster Presenter
avatar for Tom Effland

Tom Effland

PhD Candidate in Computer Science, Columbia Engineering
Effland is an  NSF IGERT "Data to Solutions"​ fellow working with Prof. Luis Gravano. He researches novel techniques for extracting actionable information on rare events from social media. He is currently working on a project finding evidence of point-source food poisoning outbreaks from Twitter, Yelp, etc. to help the NYC Department of Health launch targeted investigations of restaurants more responsively.

Demo/Poster Collaborator
FP

Fotis Psallidas

PhD, Computer Science, Columbia University

Wednesday April 6, 2016 2:00pm - 4:30pm
Roone Arledge Auditorium Lerner Hall, Columbia University 2920 Broadway, New York, NY 10040

Attendees (2)