Paper Info
Title
Real-time Local Topic Extraction using Density-based Adaptive Spatiotemporal Clustering for Enhancing Local Situation Awareness.
Abstract
In the era of big data, we are witnessing the rapid growth of a new type of information source. In particular, tweets are one of the most widely used microblogging services for situation awareness during emergencies. In our previous work, we focused on geotagged tweets posted on Twitter that included location information as well as a time and text message. We previously developed a real-time analysis system using the (ε,τ)-density-based adaptive spatiotemporal clustering algorithm to analyze local topics and events. The proposed spatiotemporal analysis system successfully detects emerging bursty areas in which geotagged tweets related to observed topics are posted actively; however the system is tailor-made and specialized for a particular observed topic, therefore, it cannot identify other topics. To address this issue, we propose a new real-time spatiotemporal analysis system for enhancing local situation awareness using a density-based adaptive spatiotemporal clustering algorithm. In the proposed system, local bursty keywords are extracted and their bursty areas are identified. We evaluated the proposed system using actual real world topics related to weather in Japan. Experimental results show that the proposed system can extract local topics and events.
Year
DOI
Venue
2015
10.5220/0005593302030210
KDIR
Keywords
Field
DocType
Spatiotemporal Analysis, Geotagged Tweets, Local Topic Extraction, Social Data Mining, Big Data Analysis, Spatiotemporal Clustering
Data science,Text message,Data mining,Social media,Situation awareness,Computer science,Microblogging,Cluster analysis,Spatiotemporal Analysis,Big data
Conference
Volume
ISBN
Citations 
01
978-1-5090-1967-0
1
PageRank 
References 
Authors
0.35
0
5
Name
Order
Citations
PageRank
Tatsuhiro Sakai1104.71
Keiichi Tamura23713.86
Shota Kotozaki310.35
Tsubasa Hayashida410.35
H. Kitakami59449.68