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Liuqing Li, & Edward A. Fox. (2019). Understanding patterns and mood changes through tweets about disasters. In Z. Franco, J. J. González, & J. H. Canós (Eds.), Proceedings of the 16th International Conference on Information Systems for Crisis Response And Management. Valencia, Spain: Iscram.
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.
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Steve Peterson, Keri Stephens, Hemant Purohit, & Amanda Hughes. (2019). When Official Systems Overload: A Framework for Finding Social Media Calls for Help during Evacuations. In Z. Franco, J. J. González, & J. H. Canós (Eds.), Proceedings of the 16th International Conference on Information Systems for Crisis Response And Management. Valencia, Spain: Iscram.
Abstract: During large-scale disasters it is not uncommon for Public Safety Answering Points (e.g., 9-1-1) to encounter
service disruptions or become overloaded due to call volume. As observed in the two past United States hurricane
seasons, citizens are increasingly turning to social media whether as a consequence of their inability to reach
9-1-1, or as a preferential means of communications. Relying on past research that has examined social media
use in disasters, combined with the practical knowledge of the first-hand disaster response experiences, this paper
develops a knowledge-driven framework containing parameters useful in identifying patterns of shared
information on social media when citizens need help. This effort explores the feasibility of determining
differences, similarities, common themes, and time-specific discoveries of social media calls for help associated
with hurricane evacuations. At a future date, validation of this framework will be demonstrated using datasets
from multiple disasters. The results will lead to recommendations on how the framework can be modified to make
it applicable as a generic disaster-type characterization tool.
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