Sifting through social media messages has become a popular way to track when and where flu cases occur, but a key hurdle hampers the process: how to identify flu-infection tweets. Some tweets are posted by people who have been sick with the virus, while others come from folks who are merely talking about the illness. If you are tracking actual flu cases, such conversations about the flu in general can skew the results. To address this problem, Johns Hopkins computer scientists and researchers in the School of Medicine have developed a new tweet-screening method that not only delivers real-time data on flu cases, but also filters out online chatter that is not linked to actual flu infections.
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Twitter allows millions of social media fans to comment in 140 characters or less on just about anything: an actor’s outlandish behavior, an earthquake’s tragic toll or the great taste of a grilled cheese sandwich. But by sifting through this busy flood of banter, is it possible to also track important public health trends? Two Johns Hopkins University computer scientists would respond with a one-word tweet: “Yes!”