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Tipping the scales: how geographical scale affects the interpretation of social media behavior in crisis research

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Listed:
  • Rachel Samuels

    (Georgia Institute of Technology)

  • Jiajia Xie

    (Georgia Institute of Technology)

  • Neda Mohammadi

    (Georgia Institute of Technology)

  • John E. Taylor

    (Georgia Institute of Technology)

Abstract

Our relationship with technology is constantly evolving, and how we use technology in disasters has evolved even faster. Understanding how to utilize human interactions with technology and the limitations of those interactions will be a crucial building block to contextualizing crisis data. The impact of geographic scale on behavioral change analyses is an unexplored facet of our ability to identify relative severities of crisis situations, magnitudes of localized crises, and total durations of disaster impacts. Within this paper, we aggregate Twitter and hurricane damage data across a wide range of geographic scales and assess the impact of increasing scale on both the recognition of extreme behaviors and the correlation between activity and damage. The power-law relationships identified between many of these variables indicate a direct, definable scalar dependence of social media aggregation analyses, and these relationships can be used to inform more intelligent, equitable, and actionable social media usage in emergency response.

Suggested Citation

  • Rachel Samuels & Jiajia Xie & Neda Mohammadi & John E. Taylor, 2022. "Tipping the scales: how geographical scale affects the interpretation of social media behavior in crisis research," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 112(1), pages 545-564, May.
    • Handle: RePEc:spr:nathaz:v:112:y:2022:i:1:d:10.1007_s11069-021-05193-8
    DOI: 10.1007/s11069-021-05193-8
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    References listed on IDEAS

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    1. Yury Kryvasheyeu & Haohui Chen & Esteban Moro & Pascal Van Hentenryck & Manuel Cebrian, 2015. "Performance of Social Network Sensors during Hurricane Sandy," PLOS ONE, Public Library of Science, vol. 10(2), pages 1-19, February.
    2. Vuong, Quang H, 1989. "Likelihood Ratio Tests for Model Selection and Non-nested Hypotheses," Econometrica, Econometric Society, vol. 57(2), pages 307-333, March.
    3. Xiangyang Guan & Cynthia Chen, 2014. "Using social media data to understand and assess disasters," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 74(2), pages 837-850, November.
    4. Rachel Samuels & John E. Taylor & Neda Mohammadi, 2020. "Silence of the Tweets: incorporating social media activity drop-offs into crisis detection," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 103(1), pages 1455-1477, August.
    5. Cynthia Chen & Dave Neal & Mengchu Zhou, 2013. "Understanding the evolution of a disaster—a Framework for Assessing Crisis in a System Environment (FACSE)," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 65(1), pages 407-422, January.
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    Cited by:

    1. Siqing Shan & Feng Zhao, 2023. "Social media-based urban disaster recovery and resilience analysis of the Henan deluge," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 118(1), pages 377-405, August.

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