Abstract
Recent research provides evidence for the presence of emergent semantics
in collaborative tagging systems. While several methods
have been proposed, little is known about the factors that influence
the evolution of semantic structures in these systems. A natural hypothesis
is that the quality of the emergent semantics depends on
the pragmatics of tagging: Users with certain usage patterns might
contribute more to the resulting semantics than others. In this work,
we propose several measures which enable a pragmatic differentiation
of taggers by their degree of contribution to emerging semantic
structures. We distinguish between categorizers, who typically
use a small set of tags as a replacement for hierarchical classification
schemes, and describers, who are annotating resources with
a wealth of freely associated, descriptive keywords. To study our
hypothesis, we apply semantic similarity measures to 64 different
partitions of a real-world and large-scale folksonomy containing
different ratios of categorizers and describers. Our results not only
show that ‘verbose’ taggers are most useful for the emergence of
tag semantics, but also that a subset containing only 40% of the
most ‘verbose’ taggers can produce results that match and even
outperform the semantic precision obtained from the whole dataset.
Moreover, the results suggest that there exists a causal link between
the pragmatics of tagging and resulting emergent semantics. This
work is relevant for designers and analysts of tagging systems interested
(i) in fostering the semantic development of their platforms,
(ii) in identifying users introducing “semantic noise”, and (iii) in
learning ontologies.
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