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When the first cases of swine flu were detected in the spring of 2009, Twitter helped to inflame the panic that spread well ahead of the disease. Useful The idea that anything could be mined from the flood of tweets reacting to the nascent threat was widely dismissed.

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"Unlike basic Internet search ... Twitter seems to have introduced too much noise into the process: As opposed to search requests, Which are generally motivated only by a desire to learn more about a given subject, too many Twitter conversations about swine flu seem to be motivated by desires to fit in, do what one 's friends do (ie tweet about it) or simply gain more popularity, "explained one NPR article.

Researchers from Google and Yahoo had already found that Certain search terms were good indicators of flu activity. Even Google had launched Google Flu Trends, Which provides public estimations for flu activity. But Twitter. it seemed, was only distracting us from reality - Helping us not Understand it.

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Yet a multi-disciplinary team of researchers at the University of Iowa had hope that Twitter could not only track the reaction to H1N1, but also track the disease itself by using contextual information in tweets that isn 't available in search terms.

"We had no idea that we would actually be able to do the second part When we started it, " explains Dr. Philip Polgreen, one member of that team. And so, it seems, Twitter could distinguished scientific an innovative tool for epidemiologists.

As the number of actual cases of flu increased to a point where estimates could be compared to reality, the team found a way to execute their plans for real-time tracking through Twitter disease. They created a program that analyzed how the Twitter stream changed as the number of actual cases reported through the Centers for Disease Control fluctuated. No human was needed to decide Which Relevant terms were - it was all done on the web. The program located Twitter terms that fluctuated in relation to actual cases, Which were often related to aspects of disease as fever SUCH temperature and doctor visits.

It then used those terms to spot signs of the flu in other geographic areas before cases were reported. The accuracy of the results demonstrated that it was possible to cut through Twitter. 's noise and locate indicators for the flu. At the time, the real-time estimations in the study were one to two weeks Faster than that of traditional flu tracking mechanisms - a time difference that matters greatly in making estimates Useful disease.

"If you want to allocate resources in an effective manner, " Polgreen says, "you really want to be able to anticipate demand ... there are huge implications on staffing and supplies. "

In a paper about the study published in April, the research team describes its results as "promising " not just for assessing disease activity, but also ancillary issues like treatment medication side effects or potential shortages. At the same time, They 're also realistic about Twitter ' s limitations for tracking disease. Not everybody uses Twitter, and there are some places where the social media tool has very few users. Twitter activity is also not constant Throughout the week, and the demographics of Twitter users is not representative of the general population.

"I don 't think this will replace traditional surveillance at all, " Polgreen says. "In fact, there 's no way to validate these traditional lines of investigation without surveillance information. But I think, and I like to hope, that it provides another stream of information. "

Image courtesy of iStockPhoto, Olena_T

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This story originally published on Mashable here.