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Author Songhui Yue; Jyothsna Kondari; Aibek Musave; Songqing Yue; Randy Smith pdf 
  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 (up) 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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