Modeling flu trends with real-time geo-tagged Twitter data streams

Jaime Chon, Ross Raymond, Haiyan Wang, Feng Wang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Citations (Scopus)

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.

Original languageEnglish (US)
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
PublisherSpringer Verlag
Pages60-69
Number of pages10
Volume9204
ISBN (Print)9783319218366
DOIs
StatePublished - 2015
Event10th International Conference on Wireless Algorithms, Systems, and Applications, WASA 2015 - Qufu, China
Duration: Aug 10 2015Aug 12 2015

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9204
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

Other10th International Conference on Wireless Algorithms, Systems, and Applications, WASA 2015
CountryChina
CityQufu
Period8/10/158/12/15

Fingerprint

Disease control
Influenza
Alarm systems
Public health
Data Streams
Statistics
Real-time
Modeling
Official Statistics
Real-time Data
Streaming Data
Early Warning
Social Media
Evaluate
Public Health
Stock Market
Correlate
Prototype
Trends
Financial markets

Keywords

  • Geo-tagged twitter stream
  • Influenza
  • Mathematical modeling

ASJC Scopus subject areas

  • Computer Science(all)
  • Theoretical Computer Science

Cite this

Chon, J., Raymond, R., Wang, H., & Wang, F. (2015). Modeling flu trends with real-time geo-tagged Twitter data streams. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9204, pp. 60-69). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9204). Springer Verlag. https://doi.org/10.1007/978-3-319-21837-3_7

Modeling flu trends with real-time geo-tagged Twitter data streams. / Chon, Jaime; Raymond, Ross; Wang, Haiyan; Wang, Feng.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 9204 Springer Verlag, 2015. p. 60-69 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9204).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Chon, J, Raymond, R, Wang, H & Wang, F 2015, Modeling flu trends with real-time geo-tagged Twitter data streams. in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). vol. 9204, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9204, Springer Verlag, pp. 60-69, 10th International Conference on Wireless Algorithms, Systems, and Applications, WASA 2015, Qufu, China, 8/10/15. https://doi.org/10.1007/978-3-319-21837-3_7
Chon J, Raymond R, Wang H, Wang F. Modeling flu trends with real-time geo-tagged Twitter data streams. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 9204. Springer Verlag. 2015. p. 60-69. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-319-21837-3_7
Chon, Jaime ; Raymond, Ross ; Wang, Haiyan ; Wang, Feng. / Modeling flu trends with real-time geo-tagged Twitter data streams. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 9204 Springer Verlag, 2015. pp. 60-69 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
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