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Discovery of local topics by using latent spatio-temporal relationships in geo-social media / Kyoung-Sook Kim in International journal of geographical information science IJGIS, vol 30 n° 9-10 (September - October 2016)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 30 n° 9-10 (September - October 2016) . - pp 1899 - 1922 Titre : Discovery of local topics by using latent spatio-temporal relationships in geo-social media Type de document : Article/Communication Auteurs : Kyoung-Sook Kim, Auteur ; Isao Kojima, Auteur ; Hirotaka Ogawa, Auteur Année de publication : 2016 Article en page(s) : pp 1899 - 1922 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] contenu généré par les utilisateurs [Termes IGN] données issues des réseaux sociaux [Termes IGN] données massives [Termes IGN] données spatiotemporelles [Termes IGN] géovisualisation [Termes IGN] positionnement automatique [Termes IGN] temps réel [Termes IGN] traitement de données Résumé : (Auteur) Social networks have played a crucial role as information channels for people to understanding their daily lives beyond merely being communication tools. In particular, coupling social networks with geographic location has boosted the worth of social media to not only enable comprehension of the effects of natural phenomena such as global warming and disasters, but also the social patterns of human societies. However, the high rate of social data generation and the large amounts of noisy data makes it difficult to directly apply social media to decision-making processes. This article proposes a new system of analyzing the spatio-temporal patterns of social phenomena in real time and the discovery of local topics based on their latent spatio-temporal relationships. We will first describe a model that represents the local patterns of populations of geo-tagged social media. We will then define a local topic whose keywords share a region in space and time and present a system implementation based on existing open source technologies. We evaluated the model of local topics with several ways of visualization in experiments and demonstrated a certain social pattern from a dataset of daily Twitter streams. The results obtained from experiments revealed certain keywords had a strong spatio-temporal proximity even though they did not occur in the same message. Numéro de notice : A2016-571 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/SOCIETE NUMERIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2016.1146956 En ligne : http://dx.doi.org/10.1080/13658816.2016.1146956 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=81715 [article]

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