From tags to trends: A first glance at social media content dynamics

Abstract

Current uncontrolled growth of online, digital multimedia content emphasizes research work on identifying trends on how this content popularity may grow over time wrt identifiable user events and interests. In this paper we analyze user-generated photos uploaded to Flickr in order to extract meaningful semantic trends covering specific geographical areas of interest. Initially, we cluster photos based on their geo-tagging metadata information and divide large areas into smaller "first level geo-clusters" of fixed size, allowing them to overlap if necessary. Within these first level geo-clusters, we identify semantically meaningful "places" of user interest, by analyzing additional textual metadata, i.e. user selected tags that characterize each place's photos. By post-processing them, we select the most appropriate tags that are able to describe landmarks and events occurring within these places of interest and examine their temporal dynamics over a long period of time. © 2012 IFIP International Federation for Information Processing.