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Author Liuqing Li; Edward A. Fox pdf  isbn
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  Title Understanding patterns and mood changes through tweets about disasters Type Conference Article
  Year 2019 Publication Proceedings of the 16th International Conference on Information Systems for Crisis Response And Management Abbreviated Journal Iscram 2019  
  Volume Issue Pages  
  Keywords Disaster, Pattern, User Classification, Mood Detection, Twitter  
  Abstract We analyzed a sample of large tweet collections gathered since 2011, to expand understanding about tweeting

patterns and emotional responses of different types of tweeters regarding disasters. We selected three examples for

each of four disaster types: school shooting, bombing, earthquake, and hurricane. For each collection, we deployed

our novel model TwiRole for user classification, and an existing deep learning model for mood detection. We

found differences in the daily tweet count patterns, between the different types of events. Likewise, there were

different average scores and patterns of moods (fear, sadness, surprise), both between types of events, and between

events of the same type. Further, regarding surprise and fear, there were differences among roles of tweeters. These

results suggest the value of further exploration as well as hypothesis testing with our hundreds of event and trend

related tweet collections, considering indications in those that reflect emotional responses to disasters.
 
  Address Virginia Tech, United States of America  
  Corporate Author Thesis  
  Publisher Iscram Place of Publication Valencia, Spain Editor Franco, Z.; González, J.J.; Canós, J.H.  
  Language English Summary Language English Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 2411-3387 ISBN 978-84-09-10498-7 Medium  
  Track T8- Social Media in Crises and Conflicts Expedition Conference (up) 16th International Conference on Information Systems for Crisis Response and Management (ISCRAM 2019)  
  Notes Approved no  
  Call Number Serial 1863  
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