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Author Liuqing Li; Edward A. Fox
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 (up)
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 16th International Conference on Information Systems for Crisis Response and Management (ISCRAM 2019)
Notes Approved no
Call Number Serial 1863
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