A new software program “nEmesis” has been developed by the researchers of University of Rochester, which can tell people about the likelihood of them falling sick after eating out at a restaurant based on tweets. Now the social media tool will help the public health experts to know the infected places and infection spreading, with use of Twitter social app.
The researchers used machine learning and crowd sourcing techniques to analyze 3.8 million tweets from some 94,000 smartphone users over a four-month period in New York City.
In the development, the system will generate the list of restaurants each day with red mark which can help the food inspectors to visit that restaurants.
"nEmesis: Which Restaurants Should You Avoid Today?" is available at http://www.cs.rochester.edu/~sadilek/publications/Sadilek-Brennan-Kautz-Silenzio_nEmesis_HCOMP-13.pdf
The researchers used machine learning and crowd sourcing techniques to analyze 3.8 million tweets from some 94,000 smartphone users over a four-month period in New York City.
How it works:
This Twitter app will help the people to flag the restaurant that made people ill after eating. nEmesis analyses people’s tweets when eating at a restaurant with the help of GPS as Pre-cise geo coordinates embedded in the messages enable them to detect specific restaurants a user had visited prior to falling ill and reveals individuals who may be suffering from a foodborne disease. After this, the system will track the individual for 72 hours and analyze the tweets during that period. If a person tweets about his/her illness and other will follow them on Twitter, it will help them system to define rating of the restaurants and their foods inspection using color-coded from low (green) to high (red).
In the development, the system will generate the list of restaurants each day with red mark which can help the food inspectors to visit that restaurants.
"nEmesis: Which Restaurants Should You Avoid Today?" is available at http://www.cs.rochester.edu/~sadilek/publications/Sadilek-Brennan-Kautz-Silenzio_nEmesis_HCOMP-13.pdf
Comments
Post a Comment