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Social media for intelligent public information and warning in disasters: An interdisciplinary review

Author

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  • Zhang, Cheng
  • Fan, Chao
  • Yao, Wenlin
  • Hu, Xia
  • Mostafavi, Ali

Abstract

Social media offers participatory and collaborative structure and collective knowledge building capacity to the public information and warning approaches. Therefore, the author envisions the intelligent public information and warning in disaster based on social media, which has three functions: (1) efficiently and effectively acquiring disaster situational awareness information, (2) supporting self-organized peer-to-peer help activities, and (3) enabling the disaster management agencies to hear from the public. To achieve this vision, authors of this study examined 304 studies conducted 2008 through 2018 to systemically evaluate the current literature in understanding the phenomena of communication on social media and the state-of-the-art studies on social media informatics in disasters. This review then identified the challenges of existing studies and proposed a research roadmap to address the challenges of achieving the vision. This review could serve as a bridge for researchers working on social media in disasters to understand the state-of-the-art of this problem in other related domains. The findings of this review highlight the value of certain research areas, e.g., (1) a fine-grained disaster social media ontology with semantic interoperability, (2) network pattern of trending information and emerging influential users, (3) fine-grained assessment of societal impacts due to infrastructure disruptions, and (4) best practices for social media usage during disasters.

Suggested Citation

  • Zhang, Cheng & Fan, Chao & Yao, Wenlin & Hu, Xia & Mostafavi, Ali, 2019. "Social media for intelligent public information and warning in disasters: An interdisciplinary review," International Journal of Information Management, Elsevier, vol. 49(C), pages 190-207.
    • Handle: RePEc:eee:ininma:v:49:y:2019:i:c:p:190-207
    DOI: 10.1016/j.ijinfomgt.2019.04.004
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    Citations

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    Cited by:

    1. Angel Petricia Vijayakumar & V. Mary Anita Rajam, 2024. "Detection and context reconstruction of sub-events that influence the course of a news event from microblog discussions," Journal of Computational Social Science, Springer, vol. 7(2), pages 1483-1517, October.
    2. Samuel Fosso Wamba, 2022. "Humanitarian supply chain: a bibliometric analysis and future research directions," Annals of Operations Research, Springer, vol. 319(1), pages 937-963, December.
    3. Carlos Galera-Zarco & Goulielmos Floros, 2024. "A deep learning approach to improve built asset operations and disaster management in critical events: an integrative simulation model for quicker decision making," Annals of Operations Research, Springer, vol. 339(1), pages 573-612, August.
    4. Shangjia Dong & Tianbo Yu & Hamed Farahmand & Ali Mostafavi, 2022. "Predictive multi-watershed flood monitoring using deep learning on integrated physical and social sensors data," Environment and Planning B, , vol. 49(7), pages 1838-1856, September.
    5. Hamed Farahmand & Wanqiu Wang & Ali Mostafavi & Mikel Maron, 2022. "Anomalous human activity fluctuations from digital trace data signal flood inundation status," Environment and Planning B, , vol. 49(7), pages 1893-1911, September.
    6. 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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