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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This section contains regularly updated highlights of the news from around The Johns Hopkins University. Links to the complete news reports from the nine schools, the Applied Physics Laboratory and other centers and institutes are to the left, as are links to help news media contact the Johns Hopkins communications offices.