https://doi.org/10.1140/epjs/s11734-024-01172-9
Review
A review of deep learning techniques for disaster management in social media: trends and challenges
Department of Computer Science and Engineering, VIT-AP University, Amaravati, Andhra Pradesh, India
Received:
11
November
2023
Accepted:
12
April
2024
Published online:
3
May
2024
In the present era, social media platforms have increasingly become invaluable sources of information and connectivity. Twitter(X) is one of the social media landscape’s most prominent and influential components. Certainly, Twitter data offers substantial value across a range of disaster-related applications. Its utility extends to real-time event detection, classifying diverse crisis types, and analyzing evolving sentiments throughout such events. A disaster is a catastrophic event that leads to significant disruption in the everyday operations of a community. This paper reviews the trends and challenges associated with using social media in disaster management. As part of this paper, we systematically and consistently examine several crises-including natural hazards, human-induced disasters, and health-related disasters. Different information types and sources are prevalent in different crises, leading to insights into their prevalence.
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© The Author(s), under exclusive licence to EDP Sciences, Springer-Verlag GmbH Germany, part of Springer Nature 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
