  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.
