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Using Twitter to track the flu: A better way to screen the Tweets
Date:1/28/2013

ould track major disease outbreaks.

This winter, as the United States entered an unusually severe and early flu season, Twitter-based flu projections have drawn increasing attention. Many public tweets, such as, "I'm so sick this week with the flu," can indicate a rise in the flu rate. Collecting enough of these tweets can help health officials gauge the scope and severity of an epidemic.

But the reliability of many computer models can be weakened by too many tweets that point to flu-related news reports and other matters not directly linked to a specific flu case, said David Broniatowski, a School of Medicine postdoctoral fellow in the Department of Emergency Medicine's Center for Advanced Modeling in the Social, Behavioral, and Health Sciences.

"For example," he said, "a recent spike in Twitter flu activity was caused by discussions about basketball legend Kobe Bryant's flu-like symptoms during a recent game. Mr. Bryant's health notwithstanding, such tweets do very little to help public health officials prepare our nation for the next big outbreak."

To improve their accuracy when using tweets to track the flu, the John Hopkins team developed sophisticated statistical methods based on human language processing technologies. The methods are designed to filter out the chatter. The system can distinguish, for example, between "I have the flu" and "I'm worried about getting the flu."

Another advantage of the Johns Hopkins flu projection method is that it can produce real-time results. By comparison, the U.S. Centers for Disease Control and Prevention, which record flu-related symptoms from hospital visits, typically take two weeks to publish data on the flu's prevalence.

To check the reliability of their enhanced system, the Johns Hopkins researchers recently compared their results to CDC data for the same period. The researchers said that during November and December 2012, their system demonstrated a substantial im
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Contact: Phil Sneiderman
prs@jhu.edu
443-287-9960
Johns Hopkins University
Source:Eurekalert  

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