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Author (up) Songhui Yue; Jyothsna Kondari; Aibek Musave; Songqing Yue; Randy Smith
Title Using Twitter Data to Determine Hurricane Category: An Experiment 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 718-726
Keywords Social Media Data; Hurricane Category; Twitter; Prediction
Abstract Social media posts contain an abundant amount of information about public opinion on major events, especially natural disasters such as hurricanes. Posts related to an event, are usually published by the users who live near the place of the event at the time of the event. Special correlation between the social media data and the events can be obtained using data mining approaches. This paper presents research work to find the mappings between social media data and the severity level of a disaster. Specifically, we have investigated the Twitter data posted during hurricanes Harvey and Irma, and attempted to find the correlation between the Twitter data of a specific area and the hurricane level in that area. Our experimental results indicate a positive correlation between them. We also present a method to predict the hurricane category for a specific area using relevant Twitter data.
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Corporate Author Thesis
Publisher Rochester Institute of Technology Place of Publication Rochester, NY (USA) Editor Kees Boersma; Brian Tomaszewski
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 sei_ysh@hotmail.com Approved no
Call Number Serial 1593
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