Density-Based Clustering in Spatial Databases: The Algorithm GDBSCAN and Its Applications
Data Mining and Knowledge Discovery
Usage patterns of collaborative tagging systems
Journal of Information Science
Exploring social annotations for the semantic web
Proceedings of the 15th international conference on World Wide Web
HT06, tagging paper, taxonomy, Flickr, academic article, to read
Proceedings of the seventeenth conference on Hypertext and hypermedia
Towards automatic extraction of event and place semantics from flickr tags
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
NET-DBSCAN: clustering the nodes of a dynamic linear network
International Journal of Geographical Information Science
Methods for extracting place semantics from Flickr tags
ACM Transactions on the Web (TWEB)
Methods for extracting place semantics from Flickr tags
ACM Transactions on the Web (TWEB)
Spatio-temporal proximity and social distance: a confirmation framework for social reporting
Proceedings of the 2nd ACM SIGSPATIAL International Workshop on Location Based Social Networks
A semantic web based gazetteer model for VGI
Proceedings of the 1st ACM SIGSPATIAL International Workshop on Crowdsourced and Volunteered Geographic Information
Uncovering locally characterizing regions within geotagged data
Proceedings of the 22nd international conference on World Wide Web
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More and more users are contributing and sharing more and more contents on the Web via the use of content hosting sites and social media services. These user-generated contents are tagged with terms characterizing the contents from the users' perspectives. Massive collections of tagged photos in popular photo hosting sites are well known for their richness in semantic extent and geospatial scope. Furthermore, geo-tags, which are machine-generated positional data, are frequently embedded within these photos. We develop in this paper an approach based on the analyses of tags and geo-tags for the exploration and characterization of the implicit localities in collections of user photos. At the same time, the approach also allows us to explore the meanings given by users about the places in their photo collections. In this approach, we first use DBSCAN (Density-based Spatial Clustering with Noise) to group geo-tagged photos into clusters (of possibly multiple distance scales). Then, a co-occurrence analysis on the tags used within a cluster is utilized to extract conceptualization of the place in the cluster. The extracted concepts are not necessarily of geospatial nature (e.g., airplane/airline names in photos taken in the surrounding area of an airport) so are especially useful when compared to concepts extracted via the simple use of readily available locational resources (e.g., gazetteers).