Authors:
Tatsuhiro Sakai
;
Keiichi Tamura
;
Shota Kotozaki
;
Tsubasa Hayashida
and
Hajime Kitakami
Affiliation:
Graduate School of Information Sciences and Hiroshima City University, Japan
Keyword(s):
Spatiotemporal Analysis, Geotagged Tweets, Local Topic Extraction, Social Data Mining, Big Data Analysis, Spatiotemporal Clustering.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Business Analytics
;
Clustering and Classification Methods
;
Data Analytics
;
Data Engineering
;
Knowledge Discovery and Information Retrieval
;
Knowledge-Based Systems
;
Soft Computing
;
Symbolic Systems
;
Web Mining
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.
(More)