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Author Takuya Oki
Title Possibility of Using Tweets to Detect Crowd Congestion: A Case Study Using Tweets just before/after the Great East Japan Earthquake Type Conference Article
Year 2018 Publication ISCRAM 2018 Conference Proceedings – 15th International Conference on Information Systems for Crisis Response and Management Abbreviated Journal Iscram 2018
Volume Issue Pages 584-596
Keywords Twitter, crowd congestion, time-series analysis, linguistic expression, disaster mitigation.
Abstract During large earthquakes, it is critical to safely guide evacuation efforts and to prevent accidents caused by congestion. In this paper, we focus on detecting the degree of crowd congestion following an earthquake based on information posted to Social Networking Services (SNSs). This research uses text data posted to Twitter just before/after the occurrence of the Great East Japan Earthquake (11 March 2011 at 02:46 PM JST). First, we extract co-occurring place names, proper nouns, and time-series information from tweets about congestion in the Tokyo metropolitan area (TMA). Next, using these extracted data, we analyze the frequency and spatiotemporal characteristics of these tweets. Finally, we identify expressions that describe the degree of crowd congestion and discuss methods to quantify these expressions based on a questionnaire survey and tweets that contain a photograph.
Address
Corporate Author Thesis
Publisher Rochester Institute of Technology Place of Publication Rochester, NY (USA) Editor Kees Boersma; Brian Tomaszeski
Language English Summary Language English Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 2411-3387 ISBN 978-0-692-12760-5 Medium
Track Social Media Studies Expedition Conference ISCRAM 2018 Conference Proceedings - 15th International Conference on Information Systems for Crisis Response and Management
Notes Approved no
Call Number Serial 2133
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