Integrating Social Media into a Pan-European Flood Awareness System: A Multilingual Approach

TL;DR

This paper presents a multilingual system that integrates social media analysis into the European Flood Awareness System, enabling real-time flood-related message collection and display across multiple languages using advanced language embedding techniques.

Contribution

It introduces a novel multilingual approach combining language-agnostic and language-aligned embeddings to classify flood-related social media messages with minimal labeled data.

Findings

Effective multilingual flood message classification achieved

Seamless integration with EFAS enhances flood awareness

Method reduces need for extensive labeled datasets

Abstract

This paper describes a prototype system that integrates social media analysis into the European Flood Awareness System (EFAS). This integration allows the collection of social media data to be automatically triggered by flood risk warnings determined by a hydro-meteorological model. Then, we adopt a multi-lingual approach to find flood-related messages by employing two state-of-the-art methodologies: language-agnostic word embeddings and language-aligned word embeddings. Both approaches can be used to bootstrap a classifier of social media messages for a new language with little or no labeled data. Finally, we describe a method for selecting relevant and representative messages and displaying them back in the interface of EFAS.