@inproceedings{cd951be3da554696a01d430ffbd8127b,
title = "Modeling flu trends with real-time geo-tagged Twitter data streams",
abstract = "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 realtime 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 realtime 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.",
keywords = "Geo-tagged twitter stream, Influenza, Mathematical modeling",
author = "Jaime Chon and Ross Raymond and Haiyan Wang and Feng Wang",
note = "Funding Information: This project is supported by NSF grant CNS #1218212. Publisher Copyright: {\textcopyright} Springer International Publishing Switzerland 2015.; 10th International Conference on Wireless Algorithms, Systems, and Applications, WASA 2015 ; Conference date: 10-08-2015 Through 12-08-2015",
year = "2015",
doi = "10.1007/978-3-319-21837-3_7",
language = "English (US)",
isbn = "9783319218366",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "60--69",
editor = "Kuai Xu and Haojin Zhu",
booktitle = "Wireless Algorithms, Systems, and Applications - 10th International Conference, WASA 2015, Proceedings",
}