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Name HASHIMOTO, Takako
Belonging department 商経学部
Occupation name 教授
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Twitter Topic Progress Visualization using Micro-clustering

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Joint Author


This paper proposes a method for visualizing the progress of a bursty topic on Twitter using a previously-proposed micro-clustering technique, which reveals the cause and the progress of a burst. Micro-clustering can efficiently represent sub-topics of a bursty topic, which allows visualizing transitions between these subtopics over time. This process allows for a Twitter user to see the origin of a bursty topic more easily. To show the method’s effectiveness, we conducted an experiment on a real bursty topic, a controversy over childcare leave in Japan. When we extract sub-topics using micro-clustering, and analyze micro-clusters over time, we can understand the progress of the target topic and discover the micro-clusters that caused the burst.


Proc. of The 10th International Conference on Pattern Recognition Applications and Methods (ICPRAM), 



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