Evaluating tagging behavior in social bookmarking systems: metrics and design heuristics

To improve existing social bookmarking systems and to design new ones, researchers and practitioners need to understand how to evaluate tagging behavior. In this paper, we analyze over two years of data from CiteULike, a social bookmarking system for tagging academic papers. We propose six tag metrics-tag growth, tag reuse, tag non-obviousness, tag discrimination, tag frequency, and tag patterns-to understand the characteristics of a social bookmarking system. Using these metrics, we suggest possible design heuristics to implement a social bookmarking system for CiteSeer, a popular online scholarly digital library for computer science. We believe that these metrics and design heuristics can be applied to social bookmarking systems in other domains.

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In  the 2007 international ACM Conference on Supporting Group Work (GROUP '07)

Publisher  Association for Computing Machinery, Inc.
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