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Algorithm Reads Tweets to Figure Out Which Restaurants Make You Sick

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The nEmesis app collected tweets in Las Vegas and evaluated them to find cases of food poisoning.

A new app uses machine learning, natural-language processing and artificial intelligence to identify tweets related to food poisoning, and to link them to specific restaurants.

Credit: Adam Sadilek/University of Rochester

University of Rochester researchers have developed nEmesis, an app that uses machine learning to help minimize the number of people affected by food poisoning.

The software uses natural-language processing and artificial intelligence to identify food poisoning-related tweets, connect them to restaurants using geotagging, and identify likely hotspots.

The researchers developed the app by analyzing nearly 4 million tweets generated by people in the New York City metropolitan area in late 2012 and early 2013. They then tested the app in Las Vegas through collaboration with the city's health department. 

For three months, nEmesis automatically scanned an average of 16,000 tweets from 3,600 users a day, and the researchers used the tweets to generate a list of the highest-priority restaurants for inspections. "Each morning we gave the city a list of places where we knew that something was wrong so they could do an inspection of those restaurants," says former University of Rochester researcher Adam Sadilek.

The researchers estimate the app resulted in 9,000 fewer food-poisoning incidents and 557 fewer hospitalizations in Las Vegas during the course of the study.

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