Unsupervised Crisis Information Extraction from Twitter Data

Interdonato Roberto, Doucet Antoine, Guillaume Jean-Loup. 2018. Unsupervised Crisis Information Extraction from Twitter Data. In : Proceedings of the 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining ASONAM 2018. IEEE, ACM. Barcelone : IEE-ACM, 579-580. ISBN 978-1-5386-6051-5 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, Barcelona, Espagne, 28 Août 2018/31 Août 2018.

Résumé : While microblogging-based Online Social Networks have become an attractive data source in emergency situations, overcoming information overload is still not trivial. We propose a framework which integrates natural language processing and clustering techniques in order to produce a ranking of relevant tweets based on their informativeness. Experiments on four Twitter collections in two languages (English and French) proved the significance of our approach.

Mots-clés libres : Crisis management, Ranking function, Natural language processing

Auteurs et affiliations

• Interdonato Roberto, CIRAD-ES-UMR TETIS (FRA) ORCID: 0000-0002-0536-6277
• Doucet Antoine, Université de La Rochelle (FRA)
• Guillaume Jean-Loup, Université de La Rochelle (FRA)

Source : Cirad-Agritrop (https://agritrop.cirad.fr/590762/)

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