TY - GEN
T1 - Twitter Topic Progress Visualization using Micro-clustering
AU - Hashimoto, Takako
AU - Kusaba, Akira
AU - Shepard, Dave
AU - Kuboyama, Tetsuji
AU - Shin, Kilho
AU - Uno, Takeaki
N1 - Funding Information:
This work was partially supported by JSPS KAK-ENHI Grant Numbers 18K11443, 19K12125, 19H01133, 19J00871, and 17H00762.
Publisher Copyright:
Copyright © 2020 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved
PY - 2020
Y1 - 2020
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85082987314&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85082987314&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:85082987314
T3 - ICPRAM 2020 - Proceedings of the 9th International Conference on Pattern Recognition Applications and Methods
SP - 585
EP - 592
BT - ICPRAM 2020 - Proceedings of the 9th International Conference on Pattern Recognition Applications and Methods
A2 - De Marsico, Maria
A2 - di Baja, Gabriella Sanniti
A2 - Fred, Ana
PB - SciTePress
T2 - 9th International Conference on Pattern Recognition Applications and Methods, ICPRAM 2020
Y2 - 22 February 2020 through 24 February 2020
ER -