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Understanding Citizens' and Local Governments' Digital Communications During Natural Disasters: The Case of Snowstorms

Published: 25 June 2017 Publication History

Abstract

A growing number of citizens and local governments have embraced the use of Twitter to communicate during natural disasters. Studies have shown that online communications during disasters can be explained using crisis communication taxonomies. However, such taxonomies are broad and general, and offer little insight into the detailed content of the communications. In this paper, we propose a semi-automatic framework to extract and compare, in retrospect, the digital communication footprints of citizens and governments during disasters. These footprints, which characterize the topics discussed during a disaster at different spatio-temporal scales, are computed in an unsupervised manner using topic models, and manually labelled to identify specific issues affecting the population. The end objective is to offer detailed information about issues affecting citizens during natural disasters and to compare these against local governments' communications. We evaluate the framework using Twitter communications from 18 snowstorms (including two blizzards) on the US east coast.

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cover image ACM Conferences
WebSci '17: Proceedings of the 2017 ACM on Web Science Conference
June 2017
438 pages
ISBN:9781450348966
DOI:10.1145/3091478
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Publication History

Published: 25 June 2017

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Author Tags

  1. crisis communication
  2. disaster analytics
  3. spatio-temporal modeling
  4. topic models

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  • Research-article

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  • National Science Foundation NSF

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WebSci '17
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WebSci '17: ACM Web Science Conference
June 25 - 28, 2017
New York, Troy, USA

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WebSci '17 Paper Acceptance Rate 30 of 85 submissions, 35%;
Overall Acceptance Rate 245 of 933 submissions, 26%

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  • (2024)Ten Traps for Non-Representational Theory in Human GeographyGeographies10.3390/geographies40200164:2(253-286)Online publication date: 18-Apr-2024
  • (2023)Using COVID-19 Vaccine Attitudes Found in Tweets to Predict Vaccine Perceptions in Traditional Surveys: Infodemiology StudyJMIR Infodemiology10.2196/437003(e43700)Online publication date: 30-Nov-2023
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  • (2021)Multi-faceted Classification for the Identification of Informative Communications during Crises: Case of COVID-192021 IEEE 45th Annual Computers, Software, and Applications Conference (COMPSAC)10.1109/COMPSAC51774.2021.00125(924-933)Online publication date: Jul-2021
  • (2021)Assessment of the adequacy of mobile applications for disaster reductionEnvironment, Development and Sustainability10.1007/s10668-021-01697-224:5(6197-6223)Online publication date: 6-Sep-2021
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