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Conference Paper: Graph topic scan statistic for spatial event detection

TitleGraph topic scan statistic for spatial event detection
Authors
Issue Date2016
PublisherACM.
Citation
The 25th ACM International on Conference on Information and Knowledge Management (CIKM 2016), Indianapolis, IN., 24-28 October 2016. In Conference Proceedings, 2016, p. 489-498 How to Cite?
AbstractSpatial event detection is an important and challenging problem. Unlike traditional event detection that focuses on the timing of global urgent event, the task of spatial event detection is to detect the spatial regions (e.g. clusters of neighboring cities) where urgent events occur. In this paper, we focus on the problem of spatial event detection using textual information in social media. We observe that, when a spatial event occurs, the topics relevant to the event are often discussed more coherently in cities near the event location than those far away. In order to capture this pattern, we propose a new method called Graph Topic Scan Statistic (Graph-TSS) that corresponds to a generalized log-likelihood ratio test based on topic modeling. We first demonstrate that the detection of spatial event regions under Graph-TSS is NP-hard due to a reduction from classical node-weighted prize-collecting Steiner tree problem (NW-PCST). We then design an efficient algorithm that approximately maximizes the graph topic scan statistic over spatial regions of arbitrary form. As a case study, we consider three applications using Twitter data, including Argentina civil unrest event detection, Chile earthquake detection, and United States influenza disease outbreak detection. Empirical evidence demonstrates that the proposed Graph-TSS performs superior over state-of-the-art methods on both running time and accuracy.
Persistent Identifierhttp://hdl.handle.net/10722/234882
ISBN

 

DC FieldValueLanguage
dc.contributor.authorLiu, Y-
dc.contributor.authorZhou, B-
dc.contributor.authorChen, F-
dc.contributor.authorCheung, DWL-
dc.date.accessioned2016-10-14T13:49:52Z-
dc.date.available2016-10-14T13:49:52Z-
dc.date.issued2016-
dc.identifier.citationThe 25th ACM International on Conference on Information and Knowledge Management (CIKM 2016), Indianapolis, IN., 24-28 October 2016. In Conference Proceedings, 2016, p. 489-498-
dc.identifier.isbn978-1-4503-4073-1-
dc.identifier.urihttp://hdl.handle.net/10722/234882-
dc.description.abstractSpatial event detection is an important and challenging problem. Unlike traditional event detection that focuses on the timing of global urgent event, the task of spatial event detection is to detect the spatial regions (e.g. clusters of neighboring cities) where urgent events occur. In this paper, we focus on the problem of spatial event detection using textual information in social media. We observe that, when a spatial event occurs, the topics relevant to the event are often discussed more coherently in cities near the event location than those far away. In order to capture this pattern, we propose a new method called Graph Topic Scan Statistic (Graph-TSS) that corresponds to a generalized log-likelihood ratio test based on topic modeling. We first demonstrate that the detection of spatial event regions under Graph-TSS is NP-hard due to a reduction from classical node-weighted prize-collecting Steiner tree problem (NW-PCST). We then design an efficient algorithm that approximately maximizes the graph topic scan statistic over spatial regions of arbitrary form. As a case study, we consider three applications using Twitter data, including Argentina civil unrest event detection, Chile earthquake detection, and United States influenza disease outbreak detection. Empirical evidence demonstrates that the proposed Graph-TSS performs superior over state-of-the-art methods on both running time and accuracy.-
dc.languageeng-
dc.publisherACM.-
dc.relation.ispartofACM International on Conference on Information and Knowledge Management, CIKM'16 Proceedings-
dc.titleGraph topic scan statistic for spatial event detection-
dc.typeConference_Paper-
dc.identifier.emailCheung, DWL: dcheung@cs.hku.hk-
dc.identifier.authorityCheung, DWL=rp00101-
dc.identifier.doi10.1145/2983323.2983744-
dc.identifier.hkuros268750-
dc.identifier.spage489-
dc.identifier.epage498-
dc.publisher.placeUnited States-
dc.customcontrol.immutablesml 161202-

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