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Visualization of Spatio-Temporal Events in Geo-Tagged Social Media

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Web and Wireless Geographical Information Systems (W2GIS 2017)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10181))

Abstract

This paper presents a spatio-temporal mapping system for visualizing a summary of geo-tagged social media as tags in a cloud, and it is associated with a web page by detecting spatio-temporal events. Through it, users can grasp events at anytime from anywhere while they browse any web pages. In order to detect spatio-temporal events from social media such as tweets, the system extracts expected events (e.g., crowded restaurants) by using machine learning algorithms to classify tweets through space and time, and it also extracts unexpected or seasonal events (e.g., time sales) by comparing the current situation to those normal regularities. Thus, the system presents a social tag cloud of tweets to help users gain a quick overview of spatio-temporal events while they browse a web page, and it also presents a tweet list to help users obtain more details about events. Furthermore, users can freely specify a time period or a tag to view its related tweets. Finally, we discuss our proposed social tag cloud generation method’s effectiveness using dense geo-tagged tweets at multi-functional buildings in urban areas.

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Notes

  1. 1.

    https://dev.twitter.com/streaming/overview.

  2. 2.

    https://html.spec.whatwg.org/multipage/comms.html#network.

  3. 3.

    https://developers.google.com/place.

  4. 4.

    http://developer.yahoo.co.jp/.

  5. 5.

    http://nlp.ist.i.kyoto-u.ac.jp/EN/index.php?JUMAN.

  6. 6.

    \(\text {Precision}=\frac{\#\text {correct answers}}{\text {total } \#\text {feature words of each category}}\).

  7. 7.

    \(\mathrm {Evaluation Ratio}=\frac{\#\text {answers of ``related to its category''}}{\#\text {answers of ``not related to its category''}}\).

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Acknowledgments

This work was partially supported by MIC SCOPE (150201013), and JSPS KAKENHI Grant Numbers 26280042, 15K00162, 16H01722, and Grants for Women Researchers of Yamaguchi University.

Author information

Authors and Affiliations

  1. Yamaguchi University, Ube City, 755-8611, Japan

    Yuanyuan Wang

  2. Kyoto Sangyo University, Kyoto City, 603-8555, Japan

    Muhammad Syafiq Mohd Pozi, Goki Yasui, Yukiko Kawai & Toyokazu Akiyama

  3. Kwansei Gakuin University, Sanda City, 669-1337, Japan

    Kazutoshi Sumiya

Authors
  1. Yuanyuan Wang
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  2. Muhammad Syafiq Mohd Pozi
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  3. Goki Yasui
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  4. Yukiko Kawai
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  5. Kazutoshi Sumiya
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  6. Toyokazu Akiyama
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Corresponding author

Correspondence to Yuanyuan Wang .

Editor information

Editors and Affiliations

  1. Naval Academy Research Institute , Brest, France

    David Brosset

  2. Naval Academy Research Institute , Brest, France

    Christophe Claramunt

  3. East Normal China University, Shanghai, China

    Xiang Li

  4. Shanghai Maritime University, Shanghai, China

    Tianzhen Wang

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Wang, Y., Mohd Pozi, M.S., Yasui, G., Kawai, Y., Sumiya, K., Akiyama, T. (2017). Visualization of Spatio-Temporal Events in Geo-Tagged Social Media. In: Brosset, D., Claramunt, C., Li, X., Wang, T. (eds) Web and Wireless Geographical Information Systems. W2GIS 2017. Lecture Notes in Computer Science(), vol 10181. Springer, Cham. https://doi.org/10.1007/978-3-319-55998-8_9

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  • DOI: https://doi.org/10.1007/978-3-319-55998-8_9

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  • Online ISBN: 978-3-319-55998-8

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