Know which restaurants to avoid

Scientists have developed a new system that tells you how likely it is for you to fall ill if you visit a particular restaurant by 'listening' to the tweets from other restaurant patrons. The University of Rochester researchers said 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.

The new system combines machine-learning and crowd-sourcing techniques to analyse millions of tweets to find people reporting food poisoning symptoms following a restaurant visit. This volume of tweets would be impossible to analyse manually, the researchers noted. 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. The system ranks restaurants according to how likely it is for someone to become ill after visiting that restaurant. "The Twitter reports are not an exact indicator - any individual case could well be due to factors unrelated to the restaurant meal - but in aggregate the numbers are revealing," said Henry Kautz, chair of the computer science department at the University of Rochester and co-author of the paper .In other words, a "seemingly random collection of online rants becomes an actionable alert," according to Kautz, which can help detect cases of food-borne illness in a timely manner.