Social media information like tweets is being used to map the current flu outbreak
Researchers are working on ways to better filter tweets for accurate real-time predictions
There are apps and tools from the CDC, Google and startups dedicated to tracking the flu
Complaining on social networks about being sick might annoy your friends and followers, but it can be useful for tools that track the spread of illnesses.
A new method for filtering tweets, developed by researchers at Johns Hopkins University, could make the real-time data pouring in more accurate.
The United States is in the middle of one of its most severe flu seasons in years. Tech companies, universities and health organizations are harnessing the wealth of data from social networks and search engines, in addition to the usual reports from vital statics offices, hospitals, doctors and public health departments, to keep the public informed and better prepare public health workers.
The Centers for Disease Control and Prevention releases a weekly influenza update for the United States that includes stats on people with flu-like symptoms, hospitalizations and deaths. But the detailed information is about two weeks old by the time it comes out.
“There are a lot of gaps in the system that Twitter can fill,” said assistant research professor Mark Dredze, who headed up the Johns Hopkins project.
Real-time information like tweets are becoming a popular source of public health information. They can be used to do more than just track outbreaks. Dredze’s goal is to get ahead of the curve and actually predict where and when illnesses will spread. This information could be invaluable for public health departments, providing them advanced warnings and time to plan with additional doctors, hospital beds or school closings. Thanks to GPS information for each tweet, the location information gathered from Twitter is more finely detailed than the CDC.
Early warnings are good for regular people as well. They push people to get vaccinated before they catch the flu, and individuals with health issues who might be more vulnerable can take extra precautions.
With an average 340 million tweets a day, Twitter is a firehouse of muddled and misleading information. Taken at face value, keywords would indicate the entire country is suffering from an ongoing fever epidemic of the Bieber variety. Much of the running commentary on Twitter is a reaction to news events, so when a flu epidemic becomes a national news story, the number of people talking about it spikes, regardless of their own health status.
“Most people have just focused on the presence of flu. The very simple thing is you look at Twitter and look at the number of people using the world flu or sick everyday,” said Dredze. “The problem with that is if you look a little more closely it doesn’t really work.”
Dredze’s team is using algorithms to correct for these issues, filtering out the noise to isolate the useful information. They were already researching using Twitter to