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

arXiv: https://arxiv.org/abs/1904.10876 · HTML: https://mlsanity.com/paper/1904.10876/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Apr 2019.

## Built on by (most cited first, 5 of 5)

- [Graph Neural Network Enhanced Language Models for Efficient Multilingual Text Classification](https://mlsanity.com/paper/2203.02912/) (2022)
- [Geo-Information Harvesting from Social Media Data](https://mlsanity.com/paper/2211.00543/) (2022)
- [Cross-Lingual Query-Based Summarization of Crisis-Related Social Media: An Abstractive Approach Using Transformers](https://mlsanity.com/paper/2204.10230/) (2022)
- [Cross-Lingual and Cross-Domain Crisis Classification for Low-Resource Scenarios](https://mlsanity.com/paper/2209.02139/) (2022)
- [Analyzing social media with crowdsourcing in Crowd4SDG](https://mlsanity.com/paper/2208.02689/) (2022)

## Abstract (arXiv)

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.