Surveying the Use of Social Media Data and Natural Language Processing Techniques to Investigate Natural Disasters
Abstract
The use of social media in crisis informatics has become increasingly popular across a range of disciplines. Leveraging natural language processing (NLP) techniques enables the analysis of textual data in novel ways and facilitates the use of social media data in disaster management. Analyzing text from social media with NLP can enhance situational awareness, accelerate information dissemination, and monitor affected communities, which is crucial for government agencies and emergency decision makers. However, a comprehensive literature review of NLP and social media data in disaster management is lacking, which presents an obstacle to rapid development in this field. To address this gap, this project surveys 324 related articles published between 2011 and 2022 and investigates the current trends and state-of-the-art NLP applications for using social media data in managing natural disasters. The bibliometric analysis reveals that existing literature focused more on earthquakes, floods, and hurricanes, concentrating on during-event periods, with Twitter (now referred to as X) as the most cited information source. Moreover, our systematic analysis identifies common NLP methodologies and identifies five major applications in current research. Finally, this study provides important insights and potential directions for future research in social media, NLP, and disaster management.
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Data Availability Statement
Some or all data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request.
Acknowledgments
This material is based upon work supported, in part, by the National Science Foundation under Grant No.1928434 and University of Maryland, College Park, research funding (start-up).
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Volume 25 • Issue 4 • November 2024
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Published online: Aug 26, 2024
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