Details
| Originalsprache | Englisch |
|---|---|
| Titel des Sammelwerks | CIKM 2012 - Proceedings of the 21st ACM International Conference on Information and Knowledge Management |
| Seiten | 2686-2688 |
| Seitenumfang | 3 |
| Publikationsstatus | Veröffentlicht - 29 Okt. 2012 |
| Veranstaltung | 21st ACM International Conference on Information and Knowledge Management, CIKM 2012 - Maui, HI, USA / Vereinigte Staaten Dauer: 29 Okt. 2012 → 2 Nov. 2012 |
Publikationsreihe
| Name | ACM International Conference Proceeding Series |
|---|
Abstract
Microblogging services, such as Twitter, are gaining interests as a means of sharing information in social networks. Numerous works have shown the potential of using Twitter posts (or tweets) in order to infer the existence and magnitude of real-world events. In the medical domain, there has been a surge in detecting public health related tweets for early warning so that a rapid response from health authorities can take place. In this paper, we present a temporal analytics tool for supporting a comparative, temporal analysis of disease outbreaks between Twitter and official sources, such as, World Health Organization (WHO) and ProMED-mail. We automatically extract and aggregate outbreak events from official outbreak reports, producing time series data. Our tool can support a correlation analysis and an understanding of the temporal developments of outbreak mentions in Twitter, based on comparisons with official sources.
ASJC Scopus Sachgebiete
- Informatik (insg.)
- Software
- Informatik (insg.)
- Mensch-Maschine-Interaktion
- Informatik (insg.)
- Maschinelles Sehen und Mustererkennung
- Informatik (insg.)
- Computernetzwerke und -kommunikation
Ziele für nachhaltige Entwicklung
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- BibTex
- RIS
CIKM 2012 - Proceedings of the 21st ACM International Conference on Information and Knowledge Management. 2012. S. 2686-2688 (ACM International Conference Proceeding Series).
Publikation: Beitrag in Buch/Bericht/Sammelwerk/Konferenzband › Aufsatz in Konferenzband › Forschung › Peer-Review
}
TY - GEN
T1 - Supporting temporal analytics for health-related events in microblogs
AU - Kanhabua, Nattiya
AU - Stewart, Avaré
AU - Nejdl, Wolfgang
AU - Romano, Sara
PY - 2012/10/29
Y1 - 2012/10/29
N2 - Microblogging services, such as Twitter, are gaining interests as a means of sharing information in social networks. Numerous works have shown the potential of using Twitter posts (or tweets) in order to infer the existence and magnitude of real-world events. In the medical domain, there has been a surge in detecting public health related tweets for early warning so that a rapid response from health authorities can take place. In this paper, we present a temporal analytics tool for supporting a comparative, temporal analysis of disease outbreaks between Twitter and official sources, such as, World Health Organization (WHO) and ProMED-mail. We automatically extract and aggregate outbreak events from official outbreak reports, producing time series data. Our tool can support a correlation analysis and an understanding of the temporal developments of outbreak mentions in Twitter, based on comparisons with official sources.
AB - Microblogging services, such as Twitter, are gaining interests as a means of sharing information in social networks. Numerous works have shown the potential of using Twitter posts (or tweets) in order to infer the existence and magnitude of real-world events. In the medical domain, there has been a surge in detecting public health related tweets for early warning so that a rapid response from health authorities can take place. In this paper, we present a temporal analytics tool for supporting a comparative, temporal analysis of disease outbreaks between Twitter and official sources, such as, World Health Organization (WHO) and ProMED-mail. We automatically extract and aggregate outbreak events from official outbreak reports, producing time series data. Our tool can support a correlation analysis and an understanding of the temporal developments of outbreak mentions in Twitter, based on comparisons with official sources.
KW - disease outbreaks
KW - event detection
KW - time series analysis
KW - Twitter
UR - http://www.scopus.com/inward/record.url?scp=84871079196&partnerID=8YFLogxK
U2 - 10.1145/2396761.2398726
DO - 10.1145/2396761.2398726
M3 - Conference contribution
AN - SCOPUS:84871079196
SN - 9781450311564
T3 - ACM International Conference Proceeding Series
SP - 2686
EP - 2688
BT - CIKM 2012 - Proceedings of the 21st ACM International Conference on Information and Knowledge Management
T2 - 21st ACM International Conference on Information and Knowledge Management, CIKM 2012
Y2 - 29 October 2012 through 2 November 2012
ER -