IDEAS home Printed from https://ideas.repec.org/a/eee/ininma/v49y2019icp190-207.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0268401218310995
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.ijinfomgt.2019.04.004?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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.
    2. 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.
    3. 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.
    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.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:ininma:v:49:y:2019:i:c:p:190-207. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: https://www.journals.elsevier.com/international-journal-of-information-management .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.