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TAGGS : grouping tweets to improve global geoparsing for disaster response / Jens A. de Bruijn in Journal of Geovisualization and Spatial Analysis, vol 2 n° 1 (June 2018)  

Public [article]  inJournal of Geovisualization and Spatial Analysis > vol 2 n° 1 (June 2018) Titre : TAGGS : grouping tweets to improve global geoparsing for disaster response Type de document : Article/Communication Auteurs : Jens A. de Bruijn, Auteur ; Hans de Moel, Auteur ; Brenden Jongman, Auteur ; et al., Auteur Année de publication : 2018 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Linguistique [Termes IGN] catastrophe naturelle [Termes IGN] données issues des réseaux sociaux [Termes IGN] Geoparsing [Termes IGN] inondation [Termes IGN] prise en compte du contexte [Termes IGN] risque naturel [Termes IGN] Twitter Résumé : (Auteur) Timely and accurate information about ongoing events are crucial for relief organizations seeking to effectively respond to disasters. Recently, social media platforms, especially Twitter, have gained traction as a novel source of information on disaster events. Unfortunately, geographical information is rarely attached to tweets, which hinders the use of Twitter for geographical applications. As a solution, geoparsing algorithms extract and can locate geographical locations referenced in a tweet’s text. This paper describes TAGGS, a new algorithm that enhances location disambiguation by employing both metadata and the contextual spatial information of groups of tweets referencing the same location regarding a specific disaster type. Validation demonstrated that TAGGS approximately attains a recall of 0.82 and precision of 0.91. Without lowering precision, this roughly doubles the number of correctly found administrative subdivisions and cities, towns, and villages as compared to individual geoparsing. We applied TAGGS to 55.1 million flood-related tweets in 12 languages, collected over 3 years. We found 19.2 million tweets mentioning one or more flood locations, which can be towns (11.2 million), administrative subdivisions (5.1 million), or countries (4.6 million). In the future, TAGGS could form the basis for a global event detection system. Numéro de notice : A2018-588 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s41651-017-0010-6 Date de publication en ligne : 26/12/2017 En ligne : https://doi.org/10.1007/s41651-017-0010-6 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92505 [article]