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Social media data and post-disaster recovery

Author

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  • Jamali, Mehdi
  • Nejat, Ali
  • Ghosh, Souparno
  • Jin, Fang
  • Cao, Guofeng

Abstract

This study introduces a multi-step methodology for analyzing social media data during the post-disaster recovery phase of Hurricane Sandy. Its outputs include identification of the people who experienced the disaster, estimates of their physical location, assessments of the topics they discussed post-disaster, analysis of the tract-level relationships between the topics people discussed and tract-level internal attributes, and a comparison of these outputs to those of people who did not experience the disaster. Faith-based, community, assets, and financial topics emerged as major topics of discussion within the context of the disaster experience. The differences between predictors of these topics compared to those of people who did not experience the disaster were investigated in depth, revealing considerable differences among vulnerable populations. The use of this methodology as a new Machine Learning Algorithm to analyze large volumes of social media data is advocated in the conclusion.

Suggested Citation

  • Jamali, Mehdi & Nejat, Ali & Ghosh, Souparno & Jin, Fang & Cao, Guofeng, 2019. "Social media data and post-disaster recovery," International Journal of Information Management, Elsevier, vol. 44(C), pages 25-37.
    • Handle: RePEc:eee:ininma:v:44:y:2019:i:c:p:25-37
    DOI: 10.1016/j.ijinfomgt.2018.09.005
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    2. Muhammad Ashraf Fauzi, 2023. "Social media in disaster management: review of the literature and future trends through bibliometric analysis," 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(2), pages 953-975, September.
    3. Xiaohong Chen & Weiwei Zhang & Xuanhua Xu & Wenzhi Cao, 2022. "Managing Group Confidence and Consensus in Intuitionistic Fuzzy Large Group Decision-Making Based on Social Media Data Mining," Group Decision and Negotiation, Springer, vol. 31(5), pages 995-1023, October.
    4. Abhinav Kumar & Jyoti Prakash Singh & Yogesh K. Dwivedi & Nripendra P. Rana, 2022. "A deep multi-modal neural network for informative Twitter content classification during emergencies," Annals of Operations Research, Springer, vol. 319(1), pages 791-822, December.

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