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GeoBurst+.
- Source :
-
ACM Transactions on Intelligent Systems & Technology . Feb2018, Vol. 9 Issue 3, p1-24. 24p. - Publication Year :
- 2018
-
Abstract
- The real-time discovery of local events (e.g., protests, disasters) has been widely recognized as a fundamental socioeconomic task. Recent studies have demonstrated that the geo-tagged tweet stream serves as an unprecedentedly valuable source for local event detection. Nevertheless, how to effectively extract local events from massive geo-tagged tweet streams in real time remains challenging. To bridge the gap, we propose a method for effective and real-time local event detection from geo-tagged tweet streams. Our method, named GeoBurst+, first leverages a novel cross-modal authority measure to identify several pivots in the query window. Such pivots reveal different geo-topical activities and naturally attract similar tweets to form candidate events. GeoBurst+ further summarizes the continuous stream and compares the candidates against the historical summaries to pinpoint truly interesting local events. Better still, as the query window shifts, GeoBurst+ is capable of updating the event list with little time cost, thus achieving continuous monitoring of the stream. We used crowdsourcing to evaluate GeoBurst+ on two million-scale datasets and found it significantly more effective than existing methods while being orders of magnitude faster. [ABSTRACT FROM AUTHOR]
- Subjects :
- *PUBLIC demonstrations
*DISASTERS
*SOCIOECONOMICS
*RIVERS
*CROWDSOURCING
Subjects
Details
- Language :
- English
- ISSN :
- 21576904
- Volume :
- 9
- Issue :
- 3
- Database :
- Academic Search Index
- Journal :
- ACM Transactions on Intelligent Systems & Technology
- Publication Type :
- Academic Journal
- Accession number :
- 128597394
- Full Text :
- https://doi.org/10.1145/3066166