Paper Info
Title
Detecting Location-Based Enumerating Bursts in Georeferenced Micro-Posts
Abstract
Nowadays, a large number of georeferenced micro-posts, i.e., short messages including location information, are posted on social media sites. People transmit and collect information over the Internet through these georeferenced micro-posts, which are usually related to not only personal topics but also local topics and events. Detecting local topics and events in georeferenced micro-posts is beneficial for many different geo-mobile application domains. Burstiness is one of the simplest and most effective criteria for extracting hot topics and events in micro-posts. In this paper, we propose a novel burst detection algorithm for detecting location-based enumerating bursts in georeferenced micro-posts. To evaluate the proposed burst detection algorithm, we used an actual set of georeferenced micro-posts, which are crawling tweets posted on the Twitter site. The experimental results show that our new burst detection algorithm can detect location-based enumerating bursts.
Year
DOI
Venue
2013
10.1109/IIAI-AAI.2013.36
IIAI-AAI
Keywords
Field
DocType
Internet,information retrieval,social networking (online),text analysis,Internet,Twitter,geomobile application domain,georeferenced micropost,location-based enumerating burst detection,social media site,burst detection,enumerating bursts,georeferenced micro-posts,spatiotemporal data,topic detection and tracking
Data mining,Crawling,Social media,Computer science,Georeference,Burstiness,The Internet
Conference
Citations 
PageRank 
References 
1
0.36
0
Authors
2
Name
Order
Citations
PageRank
Keiichi Tamura13713.86
H. Kitakami29449.68