Can Twitter Predict Where You'll Get Food Poisoning?

In 2011, one in six Americans, or 48 million people, suffered from some kind of foodborne illnesses, according to the Centers for Disease Control and Prevention. A team of computer scientists from the University of Rochester believe these high numbers are completely preventable. That's the motivation behind nEmesis -- a system that can track food poisoning by parsing mountains of geo-tagged data from Twitter.

The foundation of nEmesis is an algorithm (based on machine learning) that can single out tweets that suggest the tweeter is sick. Using crowdsourced workers in Amazon’s Mechanical Turk program, the algorithm is further trained to spot tweets from people likely to have contracted foodborne illnesses. Running with this constantly fine-tuned "brain," nEmesis "listens" to tweets tagged at verified restaurant locations and tracks the same users' tweets for 72 hours. If any of these users tweet about feeling ill, nEmesis captures this information and traces it back to where the person had eaten.