Leonor Sierra of The University of Rochester reports, “A new system could tell you how likely it is for you to become ill if you visit a particular restaurant by ‘listening’ to the tweets from other restaurant patrons. The University of Rochester researchers say their system, nEmesis, can help people make more informed decisions, and it also has the potential to complement traditional public health methods for monitoring food safety, such as restaurant inspections. For example, it could enable what they call ‘adaptive inspections,’ inspections guided in part by the real-time information that nEmesis provides.”

Sierra continues, “The system combines machine-learning and crowdsourcing techniques to analyze millions of tweets to find people reporting food poisoning symptoms following a restaurant visit. This volume of tweets would be impossible to analyze manually, the researchers note. Over a four-month period, the system collected 3.8 million tweets from more than 94,000 unique users in New York City, traced 23,000 restaurant visitors, and found 480 reports of likely food poisoning. They also found they correlate fairly well with public inspection data by the local health department, as the researchers describe in a paper to be presented at the Conference on Human Computation & Crowdsourcing in Palm Springs, Calif., in November. The system ranks restaurants according to how likely it is for someone to become ill after visiting that restaurant.”

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Image: Courtesy University of Rochester