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
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\(\text {Precision}=\frac{\#\text {correct answers}}{\text {total } \#\text {feature words of each category}}\).
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\(\mathrm {Evaluation Ratio}=\frac{\#\text {answers of ``related to its category''}}{\#\text {answers of ``not related to its category''}}\).
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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.
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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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