Twitter Topic Progress Visualization using Micro-clustering

Takako Hashimoto, Akira Kusaba, Dave Shepard, Tetsuji Kuboyama, Kilho Shin, Takeaki Uno

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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.

Original languageEnglish
Title of host publicationICPRAM 2020 - Proceedings of the 9th International Conference on Pattern Recognition Applications and Methods
EditorsMaria De Marsico, Gabriella Sanniti di Baja, Ana Fred
PublisherSciTePress
Pages585-592
Number of pages8
ISBN (Electronic)9789897583971
Publication statusPublished - 2020
Externally publishedYes
Event9th International Conference on Pattern Recognition Applications and Methods, ICPRAM 2020 - Valletta, Malta
Duration: Feb 22 2020Feb 24 2020

Publication series

NameICPRAM 2020 - Proceedings of the 9th International Conference on Pattern Recognition Applications and Methods

Conference

Conference9th International Conference on Pattern Recognition Applications and Methods, ICPRAM 2020
CountryMalta
CityValletta
Period2/22/202/24/20

All Science Journal Classification (ASJC) codes

  • Computer Vision and Pattern Recognition

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