The debate within the Web community over the optimal means
by which to organize information often pits formalized classications
against distributed collaborative tagging systems. A number
of questions remain unanswered, however, regarding the nature of
collaborative tagging systems including whether coherent categorization
schemes can emerge from unsupervised tagging by users.
This paper uses data from the social bookmarking site del.icio.us to
examine the dynamics of collaborative tagging systems. In particular,
we examine whether the distribution of the frequency of use
of tags for popular sites with a long history (many tags and many
users) can be described by a power law distribution, often characteristic
of what are considered complex systems. We produce a
generative model of collaborative tagging in order to understand
the basic dynamics behind tagging, including how a power law distribution
of tags could arise. We empirically examine the tagging
history of sites in order to determine how this distribution arises
over time and to determine the patterns prior to a stable distribution.
Lastly, by focusing on the high-frequency tags of a site where
the distribution of tags is a stabilized power law, we show how tag
co-occurrence networks for a sample domain of tags can be used
to analyze the meaning of particular tags given their relationship to
other tags.
%0 Conference Paper
%1 Halpin07taggingDynamics
%A Halpin, Harry
%A Robu, Valentin
%A Shepherd, Hana
%B WWW '07: Proceedings of the 16th international conference on World Wide Web
%C New York, NY, USA
%D 2007
%I ACM
%K generative_model memory sota_chi10_1 sota_mcitn2 tagging
%P 211--220
%R http://doi.acm.org/10.1145/1242572.1242602
%T The complex dynamics of collaborative tagging
%U http://portal.acm.org/citation.cfm?id=1242602
%X The debate within the Web community over the optimal means
by which to organize information often pits formalized classications
against distributed collaborative tagging systems. A number
of questions remain unanswered, however, regarding the nature of
collaborative tagging systems including whether coherent categorization
schemes can emerge from unsupervised tagging by users.
This paper uses data from the social bookmarking site del.icio.us to
examine the dynamics of collaborative tagging systems. In particular,
we examine whether the distribution of the frequency of use
of tags for popular sites with a long history (many tags and many
users) can be described by a power law distribution, often characteristic
of what are considered complex systems. We produce a
generative model of collaborative tagging in order to understand
the basic dynamics behind tagging, including how a power law distribution
of tags could arise. We empirically examine the tagging
history of sites in order to determine how this distribution arises
over time and to determine the patterns prior to a stable distribution.
Lastly, by focusing on the high-frequency tags of a site where
the distribution of tags is a stabilized power law, we show how tag
co-occurrence networks for a sample domain of tags can be used
to analyze the meaning of particular tags given their relationship to
other tags.
%@ 978-1-59593-654-7
@inproceedings{Halpin07taggingDynamics,
abstract = {The debate within the Web community over the optimal means
by which to organize information often pits formalized classications
against distributed collaborative tagging systems. A number
of questions remain unanswered, however, regarding the nature of
collaborative tagging systems including whether coherent categorization
schemes can emerge from unsupervised tagging by users.
This paper uses data from the social bookmarking site del.icio.us to
examine the dynamics of collaborative tagging systems. In particular,
we examine whether the distribution of the frequency of use
of tags for popular sites with a long history (many tags and many
users) can be described by a power law distribution, often characteristic
of what are considered complex systems. We produce a
generative model of collaborative tagging in order to understand
the basic dynamics behind tagging, including how a power law distribution
of tags could arise. We empirically examine the tagging
history of sites in order to determine how this distribution arises
over time and to determine the patterns prior to a stable distribution.
Lastly, by focusing on the high-frequency tags of a site where
the distribution of tags is a stabilized power law, we show how tag
co-occurrence networks for a sample domain of tags can be used
to analyze the meaning of particular tags given their relationship to
other tags.},
added-at = {2009-09-11T13:03:01.000+0200},
address = {New York, NY, USA},
author = {Halpin, Harry and Robu, Valentin and Shepherd, Hana},
biburl = {https://www.bibsonomy.org/bibtex/22c2c689cb9946670785d0940e9dab324/tobold},
booktitle = {WWW '07: Proceedings of the 16th international conference on World Wide Web},
doi = {http://doi.acm.org/10.1145/1242572.1242602},
interhash = {0a44c162c87ebd3186879a070d2c8c9d},
intrahash = {2c2c689cb9946670785d0940e9dab324},
isbn = {978-1-59593-654-7},
keywords = {generative_model memory sota_chi10_1 sota_mcitn2 tagging},
location = {Banff, Alberta, Canada},
pages = {211--220},
publisher = {ACM},
timestamp = {2009-12-21T15:58:03.000+0100},
title = {The complex dynamics of collaborative tagging},
url = {http://portal.acm.org/citation.cfm?id=1242602},
year = 2007
}