User-driven geo-temporal density-based exploration of periodic and not periodic events reported in social networks

Abstract : In this paper we propose a procedure consisting of a first collection phase of social net- work messages, a subsequent user query selection, and finally a clustering phase, de- fined by extending the density-based DBSCAN algorithm, for performing a geographic and temporal exploration of a collection of items, in order to reveal and map their latent spatio-temporal structure. Specifically, both several geo-temporal distance measures and a density-based geo-temporal clustering algorithm are proposed. The approach can be applied to social messages containing an explicit geographic and temporal location. The algorithm usage is exemplified to identify geographic regions where many geotagged Twitter messages about an event of interest have been created, possibly in the same time period in the case of non-periodic events (aperiodic events), or at regular timestamps in the case of periodic events. This allows discovering the spatio-temporal periodic and aperiodic characteristics of events occurring in specific geographic areas, and thus increasing the awareness of decision makers who are in charge of territorial planning. Several case studies are used to illustrate the proposed procedure.
Type de document :
Article dans une revue
Information Sciences, Elsevier, 2016, 340, pp.122-143. 〈10.1016/j.ins.2016.01.014〉
Liste complète des métadonnées

Littérature citée [35 références]  Voir  Masquer  Télécharger

https://hal-lirmm.ccsd.cnrs.fr/lirmm-01275619
Contributeur : Dino Ienco <>
Soumis le : mercredi 17 février 2016 - 17:54:31
Dernière modification le : jeudi 24 mai 2018 - 15:59:25
Document(s) archivé(s) le : mercredi 18 mai 2016 - 13:08:49

Fichier

geotemporalExplorationTwitter....
Fichiers produits par l'(les) auteur(s)

Identifiants

Citation

Paolo Arcaini, Gloria Bordogna, Dino Ienco, Simone Sterlacchini. User-driven geo-temporal density-based exploration of periodic and not periodic events reported in social networks. Information Sciences, Elsevier, 2016, 340, pp.122-143. 〈10.1016/j.ins.2016.01.014〉. 〈lirmm-01275619〉

Partager

Métriques

Consultations de la notice

144

Téléchargements de fichiers

188