Liuqing Li, & Edward A. Fox. (2020). Disaster Response Patterns across Different User Groups on Twitter: A Case Study during Hurricane Dorian. In Amanda Hughes, Fiona McNeill, & Christopher W. Zobel (Eds.), ISCRAM 2020 Conference Proceedings – 17th International Conference on Information Systems for Crisis Response and Management (pp. 838–848). Blacksburg, VA (USA): Virginia Tech.
Abstract: We conducted a case study analysis of disaster response patterns across different user groups during Hurricane Dorian in 2019. We built a tweet collection about the hurricane, covering a two week period. We divided Twitter users into two groups: brand/organization or individual. We found a significant difference in response patterns between the groups. Brand users increasingly participated as the disaster unfolded, and they posted more tweets than individual users on average. Regarding emotions, brand users posted more tweets with joy and surprise, while individual users posted more tweets with sadness. Fear was a common emotion between the two groups. Further, both groups used different types of hashtags and words in their tweets. Some distinct patterns were also discovered in their concerns on specific topics. These results suggest the value of further exploration with more tweet collections, considering the behavior of different user groups during disasters.
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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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