Tracking Real-Time Health With Twitter Data Serves As An Early Warning System

As the open source ethic has changed the way that we share and develop resources, crowdsourcing is redefining how we can create new resources based upon that willingness to share. One example of crowdsourcing at work for the betterment of us all is public health researchers turning to Twitter to collect real-time data about public health.

Researchers at Johns Hopkins University have developed a new software algorithm that allows them to filter the tweet stream for health references, then sort the results into health categories. Researchers Mark Dredze and Michael Paul tested their algorithm on two billion tweets, then analyzed the resulting 1.5 million health-related messages. They found patterns related to flu, allergies, insomnia, depression, cancer, pain and several other ailments. Because location information is available for tweets sent from GPS-equipped mobile devices, the researchers were able to pinpoint the origin of many health messages. The goal of the project is to predict when and where illness will spread in order to give local public health departments time to plan and allocate resources. Dredze and Paul believe that using Twitter data provides a more current snapshot of public health than is currently available from the Center for Disease Control (CDC).