Real-Time Digital Flu Surveillance using Twitter Data

Social media is producing massive amounts of data at an unprecedented scale, where people share their experiences and opinions on a variety of different things, including healthcare-related topics, like health conditions, their symptoms, treatments, side-effects, and so on. This makes the publicly available social media data an invaluable resource for mining such data to discover interesting and actionable healthcare insights. In this paper, we describe an online resource for real-time surveillance of flu that we have developed using spatial, temporal, and text mining on Twitter data. The real-time analysis results are subsequently reported visually in terms of a US flu surveillance map, distribution and timelines of flu types, flu symptoms, and flu treatments, in addition to overall flu activity timeline. Such a surveillance system can be very useful for early prediction of flu outbreaks, which in turn can facilitate faster and better response preparation. Further, the resulting insights are also expected to be very useful for both patients and doctors to make informed decisions.