Analyzing Flickr metadata to extract location-based information and semantically organize its photo content

dc.contributor.author	Σπύρου, Ευάγγελος	el
dc.contributor.author	Μυλωνάς, Φοίβος	el
dc.date.accessioned	2015-06-07T08:26:50Z
dc.date.available	2015-06-07T08:26:50Z
dc.date.issued	2015-06-07
dc.identifier.uri	http://hdl.handle.net/11400/15413
dc.rights	Αναφορά Δημιουργού-Μη Εμπορική Χρήση-Όχι Παράγωγα Έργα 3.0 Ηνωμένες Πολιτείες	*
dc.rights.uri	http://creativecommons.org/licenses/by-nc-nd/3.0/us/	*
dc.source	http://www.sciencedirect.com/science/article/pii/S0925231215005974	en
dc.subject	Information processing
dc.subject	Tags
dc.subject	Επεξεργασία πληροφοριών
dc.subject	Ετικέτες
dc.title	Analyzing Flickr metadata to extract location-based information and semantically organize its photo content	en
heal.type	journalArticle
heal.classification	Computer science
heal.classification	Computer programming
heal.classification	Πληροφορική
heal.classification	Προγραμματισμός
heal.classificationURI	http://data.seab.gr/concepts/77de68daecd823babbb58edb1c8e14d7106e83bb
heal.classificationURI	http://skos.um.es/unescothes/C00749
heal.classificationURI	N/A-Πληροφορική
heal.classificationURI	N/A-Προγραμματισμός
heal.identifier.secondary	doi:10.1016/j.neucom.2014.12.104
heal.language	en
heal.access	campus
heal.recordProvider	Τ.Ε.Ι Αθήνας. Σχολή Τεχνολογικών Εφαρμογών. Τμήμα Μηχανικών Πληροφορικής Τ.Ε.	el
heal.publicationDate	2015
heal.bibliographicCitation	Spyrou, E. and Mylonas, P. (2015). Analyzing Flickr metadata to extract location-based information and semantically organize its photo content. Neurocomputing. Elsevier B.V: 2015. Available from: http://www.sciencedirect.com/science/article/pii/S0925231215005974 [Accessed 29/05/2015]	en
heal.abstract	The first step towards efficient social media content analysis is to understand it and identify means of user interaction. Trying to study the problem from the user perspective, we analyze user-generated photos uploaded to famous Flickr social network, in order to extract meaningful semantic trends covering specific research aspects, like content popularity, spatial areas of interest and popular events. Initially, we select a geographical area of social interest, like a city center, defined by a strict bounding box. We then cluster photos taken within the box based on their geo-tagging metadata information (i.e., their latitude and longitude information) and divide large areas into smaller groups of fixed size, which we will refer to as “geo-clusters”. Within these geo-clusters, we further identify semantically meaningful “places” of user interest, by analyzing any additional textual metadata available, i.e., user selected tags that characterize each place's photos. By post-processing the latter, we are then able to rank them and thus select the most appropriate tags that describe landmarks and other places of interest, as well as events occurring within these places of interest. As a next step, we place these tags on a map and help users to intuitively visualize places of interest and the actual photo content at a glance. Finally, we examine the temporal dynamics of analyzed photos over a long period of time, so as to obtain the underlying trends to be identified within this kind of social media generated content.	en
heal.publisher	Elsevier B.V	en
heal.journalName	Neurocomputing	en
heal.journalType	peer-reviewed
heal.fullTextAvailability	true