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Real-time social media sentiment analysis for rapid impact assessment of floods

Bryan-Smith, Lydia; Godsall, Jake; George, Franky; Egode, Kelly; Dethlefs, Nina; Parsons, Dan


Lydia Bryan-Smith

Jake Godsall

Franky George

Kelly Egode

Dan Parsons


Traditional approaches to flood modelling mostly rely on hydrodynamic physical simulations. While these simulations can be accurate, they are computationally expensive and prohibitively so when thinking about real-time prediction based on dynamic environmental conditions. Alternatively, social media platforms such as Twitter are often used by people to communicate during a flooding event, but discovering which tweets hold useful information is the key challenge in extracting information from posts in real time. In this article, we present a novel model for flood forecasting and monitoring that makes use of a transformer network that assesses the severity of a flooding situation based on sentiment analysis of the multimodal inputs (text and images). We also present an experimental comparison of a range of state-of-the-art deep learning methods for image processing and natural language processing. Finally, we demonstrate that information induced from tweets can be used effectively to visualise fine-grained geographical flood-related information dynamically and in real-time.


Bryan-Smith, L., Godsall, J., George, F., Egode, K., Dethlefs, N., & Parsons, D. (2023). Real-time social media sentiment analysis for rapid impact assessment of floods. Computers & geosciences, 178, Article 105405.

Journal Article Type Article
Acceptance Date Jun 17, 2023
Online Publication Date Jun 28, 2023
Publication Date Sep 1, 2023
Deposit Date Aug 23, 2023
Publicly Available Date Aug 24, 2023
Journal Computers and Geosciences
Print ISSN 0098-3004
Publisher Elsevier
Peer Reviewed Peer Reviewed
Volume 178
Article Number 105405
Keywords Social media; Sentiment analysis; Flooding; Artificial intelligence
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