Researchers at the University of Rochester in New York are using Twitter to track how factors such as social status, exposure to pollution and even taking the bus or going to the gym influence one's health.

By following thousands of Twitter users in New York, many of whom sent tweets that were location tagged, over a period of a month, researchers were able to estimate interactions between users and their environment.

This information allowed the researchers to map out not only what people said in their tweets but where and when they were in a specific location, collating information such as how often particular Twitter users took the subway, went to a gym or a particular restaurant, their proximity to sources of pollution and their online social status.

As Adam Sadilek, postdoctoral researcher at the University of Rochester explained, by using Twitter in this manner it allows researchers to gather information "passively, quickly and inexpensively."

The study concludes that, not surprisingly, pollution sources appear to have a negative effect on health. But more importantly the findings also show that any activity that involves human contact increases one's chances of becoming ill, including going to the gym.

Perhaps the most surprising finding was that Twitter users who merely tweet about going to the gym, but actually never go (verified by their GPS), get sick significantly more often than those who don't.

As a result of the data gathered and the mapping model that was created from the findings, Sadilek and colleague Professor Henry Kautz have developed a web application called GermTracker.

This web application uses the user's location to color-code (from red to green) their area in terms of health by relying on information taken from tweets in 10 cities around the world. The map shows circles of varying colors representing tweets, and visitors to the map can click on the tweet and then determine whether or not the information contained within the tweet indicates that that Twitter user is sick, thereby helping the app "learn."

So, for example, a tweet sent in London saying "That is sick man" would not indicate that the person was ill, whereas one saying "I'm so sick I've been in bed all week" would.

The researchers have recently started two collaborations with scientists at the University of Rochester Medical Center linking tweets to influenza studies and another working on linking tweets to depression and psychological disorders.

Check out GermTracker at: