document: Stop thinking , start tagging : tag semantics emerge from collaborative verbosity 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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