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.

本文介绍了一个将社交媒体分析集成到欧洲洪水预警系统（EFAS）中的原型系统。该系统能够自动触发社交媒体数据的收集，并采用多语言方法通过语言不可知的单词嵌入和语言对齐的单词嵌入来找到与洪水相关的消息。同时，作者们还提出了一种选择相关和代表性消息并在 EFAS 界面中显示的方法。