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

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.

Automated summary

Traditional flood modelling relies on costly hydrodynamic physical simulations, while social media platforms like Twitter are used for real-time communication during flooding events. This article introduces a novel flood forecasting and monitoring model that uses a transformer network to analyze the sentiment of multimodal inputs (text and images) in order to assess flooding severity. The article also compares state-of-the-art deep learning methods for image and natural language processing. Additionally, the article demonstrates the effective use of information from tweets to dynamically visualize detailed geographical flood-related information.