English   español  
Por favor, use este identificador para citar o enlazar a este item: http://hdl.handle.net/10261/161333
logo share SHARE logo core CORE   Add this article to your Mendeley library MendeleyBASE

Visualizar otros formatos: MARC | Dublin Core | RDF | ORE | MODS | METS | DIDL
Exportar a otros formatos:

Tweet-SCAN: An event discovery technique for geo-located tweets

AutorCapdevila, Joan ; Cerquides, Jesús ; Nin, Jordi; Torres, Jordi
Palabras claveTwitter
Hierarchical Dirichlet Process (HDP)
Probabilistic topic models
Unsupervised learning
Event discovery
Fecha de publicación2017
CitaciónPattern Recognition Letters 93: 58- 68 (2017)
ResumenTwitter has become one of the most popular Location-based Social Networks (LBSNs) that bridges physical and virtual worlds. Tweets, 140-character-long messages, are aimed to give answer to the What¿s happening? question. Occurrences and events in the real life (such as political protests, music concerts, natural disasters or terrorist acts) are usually reported through geo-located tweets by users on site. Uncovering event-related tweets from the rest is a challenging problem that necessarily requires exploiting different tweet features. With that in mind, we propose Tweet-SCAN, a novel event discovery technique based on the popular density-based clustering algorithm called DBSCAN. Tweet-SCAN takes into account four main features from a tweet, namely content, time, location and user to group together event-related tweets. The proposed technique models textual content through a probabilistic topic model called Hierarchical Dirichlet Process and introduces Jensen¿Shannon distance for the task of neighborhood identification in the textual dimension. As a matter of fact, we show Tweet-SCAN performance in two real data sets of geo-located tweets posted during Barcelona local festivities in 2014 and 2015, for which some of the events were identified by domain experts beforehand. Through these tagged data sets, we are able to assess Tweet-SCAN capabilities to discover events, justify using a textual component and highlight the effects of several parameters. © 2016 Elsevier B.V.
Identificadoresdoi: 10.1016/j.patrec.2016.08.010
issn: 0167-8655
Aparece en las colecciones: (IIIA) Artículos
Ficheros en este ítem:
Fichero Descripción Tamaño Formato  
accesoRestringido.pdf15,38 kBAdobe PDFVista previa
Mostrar el registro completo

Artículos relacionados:

NOTA: Los ítems de Digital.CSIC están protegidos por copyright, con todos los derechos reservados, a menos que se indique lo contrario.