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Discovering and Characterizing Places of Interest Using Flickr and Twitter

Discovering and Characterizing Places of Interest Using Flickr and Twitter

Steven Van Canneyt, Steven Schockaert, Bart Dhoedt
Copyright: © 2013 |Volume: 9 |Issue: 3 |Pages: 28
ISSN: 1552-6283|EISSN: 1552-6291|EISBN13: 9781466633438|DOI: 10.4018/ijswis.2013070105
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MLA

Van Canneyt, Steven, et al. "Discovering and Characterizing Places of Interest Using Flickr and Twitter." IJSWIS vol.9, no.3 2013: pp.77-104. http://doi.org/10.4018/ijswis.2013070105

APA

Van Canneyt, S., Schockaert, S., & Dhoedt, B. (2013). Discovering and Characterizing Places of Interest Using Flickr and Twitter. International Journal on Semantic Web and Information Systems (IJSWIS), 9(3), 77-104. http://doi.org/10.4018/ijswis.2013070105

Chicago

Van Canneyt, Steven, Steven Schockaert, and Bart Dhoedt. "Discovering and Characterizing Places of Interest Using Flickr and Twitter," International Journal on Semantic Web and Information Systems (IJSWIS) 9, no.3: 77-104. http://doi.org/10.4018/ijswis.2013070105

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Abstract

Databases of places have become increasingly popular to identify places of a given type that are close to a user-specified location. As it is important for these systems to use an up-to-date database with a broad coverage, there is a need for techniques that are capable of expanding place databases in an automated way. In this paper the authors discuss how geographically annotated information obtained from social media can be used to discover new places. In particular, the authors first determine potential places of interest by clustering the locations where Flickr photos have been taken. The tags from the Flickr photos and the terms of the Twitter messages posted in the vicinity of the obtained candidate places of interest are then used to rank them based on the likelihood that they belong to a given type. For several place types, their methodology finds places that are not yet contained in the databases used by Foursquare, Google, LinkedGeoData and Geonames. Furthermore, the authors’ experimental results show that the proposed method can successfully identify errors in existing place databases such as Foursquare.

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