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Author
Songhui Yue
;
Jyothsna Kondari
;
Aibek Musaev
;
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.
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
2145
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