Washington, Feb 8 (IANS) Social networking site Twitter takes the sweat out of health and lifestyle surveys bearing on social status, exposure to pollution, and interpersonal interaction, shows a study.
"Twitter and the technology we have developed allow us to do this passively, quickly and inexpensively; we can listen in to what people are saying and use this data to make predictions," said Adam Sadilek, study co-author and postdoctoral researcher at the University of Rochester.
"If you want to know, down to the individual level, how many people are sick in a population, you would have to survey the population, which is costly and time-consuming," he said, according to a Rochester statement.
The technology that Sadilek and his colleague Henry Kautz, professor, have developed has led to a web application called GermTracker.
The application colour-codes users (from red to green) according to their health by mining information from their tweets for 10 cities worldwide.
Using tweets collected in New York City over a period of a month, they looked at factors like how often a person takes the subway, goes to the gym or a particular restaurant, proximity to a pollution source and their online social status.
Even people who regularly go to the gym get sick marginally more often than less active individuals.
However, people who merely talk about going to the gym, but actually never go (verified based on their GPS), get sick significantly more often.
This shows that there are interesting confounding factors that can now be studied at scale.
Using the GPS data encoded in the tweets, the application can then place people on a map which allows anyone using the application to see their distribution.
Sadilek also explained that many tweets are geo-tagged, which means they carry GPS information that shows exactly where the user was when he or she tweeted.
These findings were presented at the International Conference on Web Searching and Data Mining in Rome, Italy.
Published Date: Feb 08, 2013 23:00 PM | Updated Date: Feb 08, 2013 23:00 PM