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

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.

References

  1. Avvenuti, M., Cresci, S., Marchetti, A., Meletti, C., and Tesconi, M. (2014). Ears (earthquake alert and report system): A real time decision support system for earthquake crisis management. In Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 1749- 1758.
  2. Ester, M., Kriegel, H.-P., Sander, J., and Xu, X. (1996). A density-based algorithm for discovering clusters in large spatial databases with noise. In Second International Conference on Knowledge Discovery and Data Mining, pages 226-231.
  3. Hui, C., Tyshchuk, Y., Wallace, W. A., Magdon-Ismail, M., and Goldberg, M. (2012). Information cascades in social media in response to a crisis: A preliminary model and a case study. In Proceedings of the 21st International Conference Companion on WWW, pages 653- 656.
  4. Hwang, M.-H., Wang, S., Cao, G., Padmanabhan, A., and Zhang, Z. (2013). Spatiotemporal transformation of social media geostreams: A case study of twitter for flu risk analysis. In Proceedings of the 4th ACM SIGSPATIAL IWGS, pages 12-21.
  5. Hyndman, R. J. and Fan, Y. (1996). Sample Quantiles in Statistical Packages. The American Statistician, 50(4):361-365.
  6. Kim, K.-S., Lee, R., and Zettsu, K. (2011). mtrend: discovery of topic movements on geo-microblogging messages. In Proceedings of the 19th ACM SIGSPATIAL International Conference on Advances in GIS, pages 529-532.
  7. Kreiner, K., Immonen, A., and Suominen, H. (2013). Crisis management knowledge from social media. In Proceedings of the 18th ADCS, pages 105-108.
  8. Kumar, A., Jiang, M., and Fang, Y. (2014). Where not to go?: Detecting road hazards using twitter. In Proceedings of the 37th International ACM SIGIR Conference on Research & Development in Information Retrieval, pages 1223-1226.
  9. Mendoza, M., Poblete, B., and Castillo, C. (2010). Twitter under crisis: Can we trust what we rt? In Proceedings of the First Workshop on SOMA, pages 71-79.
  10. Naaman, M. (2011). Geographic information from georeferenced social media data. SIGSPATIAL Special, 3(2):54-61.
  11. Sakaki, T., Okazaki, M., and Matsuo, Y. (2010). Earthquake shakes twitter users: Real-time event detection by social sensors. In Proceedings of the 19th International Conference on WWW, pages 851-860.
  12. Sander, J., Ester, M., Kriegel, H.-P., and Xu, X. (1998). Density-based clustering in spatial databases: The algorithm gdbscan and its applications. Data Mining and Knowledge Discovery, 2(2):169-194.
  13. Thom, D., Bosch, H., Koch, S., Worner, M., and Ertl, T. (2012). Spatiotemporal anomaly detection through visual analysis of geolocated twitter messages. In Pacific Visualization Symposium (PacificVis), 2012 IEEE, pages 41-48.
  14. Vieweg, S., Hughes, A. L., Starbird, K., and Palen, L. (2010). Microblogging during two natural hazards events: What twitter may contribute to situational awareness. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pages 1079-1088.
  15. Yin, J., Lampert, A., Cameron, M., Robinson, B., and Power, R. (2012). Using social media to enhance emergency situation awareness. IEEE Intelligent Systems, 27(6):52-59.
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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