Usage patterns of collaborative tagging systems
Journal of Information Science
HT06, tagging paper, taxonomy, Flickr, academic article, to read
Proceedings of the seventeenth conference on Hypertext and hypermedia
Towards better understanding of folksonomic patterns
Proceedings of the eighteenth conference on Hypertext and hypermedia
Evaluating tagging behavior in social bookmarking systems: metrics and design heuristics
Proceedings of the 2007 international ACM conference on Supporting group work
The Benefit of Using Tag-Based Profiles
LA-WEB '07 Proceedings of the 2007 Latin American Web Conference
A Semantic Imitation Model of Social Tag Choices
CSE '09 Proceedings of the 2009 International Conference on Computational Science and Engineering - Volume 04
Folks in Folksonomies: social link prediction from shared metadata
Proceedings of the third ACM international conference on Web search and data mining
Why do people tag?: motivations for photo tagging
Communications of the ACM
Ecological Rationality: Intelligence in the World
Ecological Rationality: Intelligence in the World
Semantic stability in social tagging streams
Proceedings of the 23rd international conference on World wide web
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While research on collaborative tagging systems has largely been the purview of computer scientists, the behavior of these systems is driven by the psychology of their users. Here we explore how simple models of boundedly rational human decision making may partly account for the high-level properties of a collaborative tagging environment, in particular with respect to the distribution of tags used across the folksonomy. We discuss several plausible heuristics people might employ to decide on tags to use for a given item, and then describe methods for testing evidence of such strategies in real collaborative tagging data. Using a large dataset of annotations collected from users of the social music website Last.fm with a novel crawling methodology (approximately one millions total users), we extract the parameters for our decision-making models from the data. We then describe a set of simple multi-agent simulations that test our heuristic models, and compare their results to the extracted parameters from the tagging dataset. Results indicate that simple social copying mechanisms can generate surprisingly good fits to the empirical data, with implications for the design and study of tagging systems.