Real-time Local Topic Extraction using Density-based Adaptive Spatiotemporal Clustering for Enhancing Local Situation Awareness

Tatsuhiro Sakai, Keiichi Tamura, Shota Kotozaki, Tsubasa Hayashida, Hajime Kitakami

2015

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.

Paper Citation

in Harvard Style

Sakai T., Tamura K., Kotozaki S., Hayashida T. and Kitakami H. (2015). Real-time Local Topic Extraction using Density-based Adaptive Spatiotemporal Clustering for Enhancing Local Situation Awareness . In Proceedings of the 7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR, (IC3K 2015) ISBN 978-989-758-158-8, pages 203-210. DOI: 10.5220/0005593302030210

in Bibtex Style

@conference{kdir15,
author={Tatsuhiro Sakai and Keiichi Tamura and Shota Kotozaki and Tsubasa Hayashida and Hajime Kitakami},
title={Real-time Local Topic Extraction using Density-based Adaptive Spatiotemporal Clustering for Enhancing Local Situation Awareness},
booktitle={Proceedings of the 7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR, (IC3K 2015)},
year={2015},
pages={203-210},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005593302030210},
isbn={978-989-758-158-8},
}

in EndNote Style

TY - CONF
JO - Proceedings of the 7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR, (IC3K 2015)
TI - Real-time Local Topic Extraction using Density-based Adaptive Spatiotemporal Clustering for Enhancing Local Situation Awareness
SN - 978-989-758-158-8
AU - Sakai T.
AU - Tamura K.
AU - Kotozaki S.
AU - Hayashida T.
AU - Kitakami H.
PY - 2015
SP - 203
EP - 210
DO - 10.5220/0005593302030210