Tag-geotag correlation in social networks

Authors:
Sang Su Lee;Dongwoo Won;Dennis McLeod
Affiliations:
University of Southern California, Los Angeles, CA, USA;University of Southern California, Los Angeles, CA, USA;University of Southern California, Los Angeles, CA, USA
Venue:
Proceedings of the 2008 ACM workshop on Search in social media
Year:
2008

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Abstract

This paper presents an analysis of the correlation of annotated information unit (textual) tags and geographical identification metadata geotags. Despite the increased usage of geotagging in collaborative tagging systems, most current research focuses on textual tagging alone in solving the tag search problem. This may result in difficulties to search for precise and relevant information within the given tag space. For example, inconsistencies like polysemy, synonyms, and word inflections with plural forms complicate the tag search problem. Therefore, more work needs to be done to include geotag information with existing tagging information for analysis. In this paper, to make geotagging possible to be used in analysis with tagging, we prove that there is a strong correlation between tagging and geotagging information. Our approach uses tag similarity and geographical distribution similarity to determine inter-relationships among tags and geotags. From our initial experiments, we show that the power law is established between tag similarity and geographical distribution similarity: this means that tag similarity and geographical distribution similarity has a strong correlation and the correlation can be used to find more relevant tags in the tag space. The power law confirms that there is an increased relationship between tagging and geotagging and the increased relationship is scalable in size of tags and geotags. Also, using both geotagging and tagging information instead of only tagging, we show that the uncertainty between derived and actual similarities among tags is reduced.