Washington: A new system has been reportedly developed to tell people about the likelihood of them falling sick after eating out at a particular restaurant based on tweets from other restaurant patrons.
Researchers at the University of Rochester have developed the system called nEmesis which can help people make more informed decisions and also has the potential to complement traditional public health methods for monitoring food safety, such as restaurant inspections.
The system combines machine-learning and crowdsourcing techniques to analyze millions of tweets to find people reporting food poisoning symptoms following a restaurant visit.
The researchers found that the tweet analysis correlates fairly well with public inspection data by the local health department and the system ranks restaurants according to how likely it is for someone to become ill after visiting that restaurant.
Co-author of the paper, Henry Kautz said that Twitter reports are not an exact indicator because any individual case could well be due to factors unrelated to the restaurant meal, but in aggregate the numbers are revealing adding that a seemingly random collection of online rants becomes an actionable alert which can help detect cases of foodborne illness in a timely manner.
The system `listens` to relevant public tweets and detects restaurant visits by matching up where a person tweets from and the known locations of restaurants and the researchers used ` crowdsourcing ` to improve the algorithm.
However, the system only considers people who tweet, who might not even be a representative sample of the whole population or of the population visiting a restaurant. But the Twitter data can be used together with knowledge gained from other sources to detect food borne illness in a timely manner.