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. We investigate the correlation between the Twitter flu counts and the official statistics from the Center for Disease Control and Prevention (CDC) and discover that real-time tweet streams capture the dynamics of influenza cases at both national and regional level and could potentially serve as an early warning system of influenza epidemics. Furthermore, we propose a dynamic mathematical model which can forecast Twitter flu counts with high accuracy.
CITATION STYLE
Wang, F., Wang, H., Xu, K., Raymond, R., Chon, J., Fuller, S., & Debruyn, A. (2016). Regional Level Influenza Study with Geo-Tagged Twitter Data. Journal of Medical Systems, 40(8). https://doi.org/10.1007/s10916-016-0545-y
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