GAZOUILLE: Detecting and Illustrating Local Events from Geolocalized Social Media Streams

被引：2

作者：

Houdyer, Pierre ^{[1
]}

Zimmerman, Albrecht ^{[2
]}

Kaytoue, Mehdi ^{[2
]}

Plantevit, Marc ^{[3
]}

Mitchell, Joseph ^{[1
]}

Robardet, Celine ^{[2
]}

机构：

[1] Tapastreet Ltd, Dublin, Ireland

[2] INSA Lyon, CNRS, LIRIS UMR5205, F-69621 Villeurbanne, France

[3] Univ Lyon 1, CNRS, LIRIS UMR5205, F-69622 Lyon, France

来源：

MACHINE LEARNING AND KNOWLEDGE DISCOVERY IN DATABASES, PT III | 2015年 / 9286卷

关键词：

D O I：

10.1007/978-3-319-23461-8_29

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

We present Gazouille, a system for discovering local events in geo-localized social media streams. The system is based on three core modules: (i) social networks data acquisition on several urban areas, (ii) event detection through time series analysis, and (iii) a Web user interface to present events discovered in real-time in a city, associated to a gallery of social media that characterize the event.

引用

页码：276 / 280

页数：5

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