Automated Emergence of a Crisis Situation Model in Crisis Response Based on Tweets

Abstract : During a crisis, being able to understand quickly the situation on-site is crucial for the responders to take relevant decisions together. Social media, in particular Twitter, have proved to be a means for rapidly getting information from the field. However, the deluge of data is heterogeneous in many ways (location, trust, content, vocabulary, etc.), and getting a model of the crisis situation still requires laborious human actions. In addition, depending on which kind of information is mined from them, tweets have to be handle one-by-one (e.g. find victims), or as a whole - amount of tweets - (e.g. occurence of an event). This paper proposes a framework for automatically extracting, interpreting and aggregating streams of tweets to characterize crisis situations. It is based on a specific metamodel that determines the different concepts required to model a crisis situation.
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Aurelie Montarnal, Shane Halse, Andrea Tapia, Sébastien Truptil, Frederick Benaben. Automated Emergence of a Crisis Situation Model in Crisis Response Based on Tweets. 18th Working Conference on Virtual Enterprises (PROVE), Sep 2017, Vicenza, Italy. pp.658-665, ⟨10.1007/978-3-319-65151-4_58 ⟩. ⟨hal-01674873⟩

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