Regional Level Influenza Study with Geo-Tagged Twitter Data.
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...
| Publicado en: | Journal of Medical Systems Vol. 40; no. 8; pp. 1 - 9 |
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| Autores principales: | , , , , , , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
Aug2016
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=116654807&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 116654807 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01485598 4N0 jtl: Journal of Medical Systems issn: 01485598 maglogo: N pubinfo: dt: Aug2016 vid: 40 iid: 8 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 116654807 10.1007/s10916-016-0545-y 116654807 ppf: 1 ppct: 8 formats: fmt: @attributes: type: P tig: atl: Regional Level Influenza Study with Geo-Tagged Twitter Data. aug: au: Feng Wang Haiyan Wang Kuai Xu Raymond, Ross Chon, Jaime Fuller, Shaun Debruyn, Anton affil: School of Mathematical and Natural Sciences, New College of Interdisciplinary Arts and Sciences, Arizona State University, Glendale, Arizona USA. sug: ab: 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. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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