Modeling Flu Trends with Real-Time Geo-tagged Twitter Data Streams
The rich data generated and read by millions of users on social media tells what is happening in the real world in a rapid and accurate fashion. In recent years many researchers have explored real-time streaming data from Twitter for a broad range of applications, including predicting stock markets and public health trend. In this paper we design, implement, and evaluate a prototype system to collect and analyze influenza statuses over different geographical locations with real-time tweet streams. To evaluate the accuracy of the influenza estimation based on tweet streams, we correlate the results with official statistics from Center for Disease Control and Prevention (CDC). Our preliminary results have demonstrated that real-time tweet streams capture the dynamics of influenza at national level, and could potentially serve as an early warning system of influenza epidemics or flu trends.
KeywordsInfluenza Mathematical modeling Geo-tagged twitter stream
This project is supported by NSF grant CNS #1218212.
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