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Use of social media data in disaster management: A survey

Lookup NU author(s): Top Phengsuwan, Dr Tejal Shah, Nipun Thekkummal, Dr Zhenyu Wen, Rui SunORCiD, Professor Graham MorganORCiD, Professor Philip James, Professor Raj Ranjan

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This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).

Abstract

© 2021 by the authors. Licensee MDPI, Basel, Switzerland.Social media has played a significant role in disaster management, as it enables the general public to contribute to the monitoring of disasters by reporting incidents related to disaster events. However, the vast volume and wide variety of generated social media data create an obstacle in disaster management by limiting the availability of actionable information from social media. Several approaches have therefore been proposed in the literature to cope with the challenges of social media data for disaster management. To the best of our knowledge, there is no published literature on social media data management and analysis that identifies the research problems and provides a research taxonomy for the classification of the common research issues. In this paper, we provide a survey of how social media data contribute to disaster management and the methodologies for social media data management and analysis in disaster management. This survey includes the methodologies for social media data classification and event detection as well as spatial and temporal information extraction. Furthermore, a taxonomy of the research dimensions of social media data management and analysis for disaster management is also proposed, which is then applied to a survey of existing literature and to discuss the core advantages and disadvantages of the various methodologies.

Publication metadata

Author(s): Phengsuwan J, Shah T, Thekkummal NB, Wen Z, Sun R, Pullarkatt D, Thirugnanam H, Ramesh MV, Morgan G, James P, Ranjan R

Publication type: Article

Publication status: Published

Journal: Future Internet

Year: 2021

Volume: 13

Issue: 2

Online publication date: 12/02/2021

Acceptance date: 09/02/2021

Date deposited: 22/03/2021

ISSN (electronic): 1999-5903

Publisher: MDPI AG

URL: https://doi.org/10.3390/fi13020046

DOI: 10.3390/fi13020046

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