Details
| Original language | English |
|---|---|
| Title of host publication | CIKM 2012 - Proceedings of the 21st ACM International Conference on Information and Knowledge Management |
| Pages | 2686-2688 |
| Number of pages | 3 |
| Publication status | Published - 29 Oct 2012 |
| Event | 21st ACM International Conference on Information and Knowledge Management, CIKM 2012 - Maui, HI, United States Duration: 29 Oct 2012 → 2 Nov 2012 |
Publication series
| 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.
Keywords
- disease outbreaks, event detection, time series analysis, Twitter
ASJC Scopus subject areas
- Computer Science(all)
- Software
- Computer Science(all)
- Human-Computer Interaction
- Computer Science(all)
- Computer Vision and Pattern Recognition
- Computer Science(all)
- Computer Networks and Communications
Sustainable Development Goals
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CIKM 2012 - Proceedings of the 21st ACM International Conference on Information and Knowledge Management. 2012. p. 2686-2688 (ACM International Conference Proceeding Series).
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › 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 -