Toward principles for the design of ontologies used for knowledge sharing
International Journal of Human-Computer Studies - Special issue: the role of formal ontology in the information technology
Usage patterns of collaborative tagging systems
Journal of Information Science
tagging, communities, vocabulary, evolution
CSCW '06 Proceedings of the 2006 20th anniversary conference on Computer supported cooperative work
Ontologies are us: A unified model of social networks and semantics
Web Semantics: Science, Services and Agents on the World Wide Web
The complex dynamics of collaborative tagging
Proceedings of the 16th international conference on World Wide Web
An epistemic dynamic model for tagging systems
Proceedings of the nineteenth ACM conference on Hypertext and hypermedia
Integrating Folksonomies with the Semantic Web
ESWC '07 Proceedings of the 4th European conference on The Semantic Web: Research and Applications
The role of tag suggestions in folksonomies
Proceedings of the 20th ACM conference on Hypertext and hypermedia
Power-Law Distributions in Empirical Data
SIAM Review
Semantic imitation in social tagging
ACM Transactions on Computer-Human Interaction (TOCHI)
A similarity measure for indefinite rankings
ACM Transactions on Information Systems (TOIS)
PINTS: peer-to-peer infrastructure for tagging systems
IPTPS'08 Proceedings of the 7th international conference on Peer-to-peer systems
Tags vs shelves: from social tagging to social classification
Proceedings of the 22nd ACM conference on Hypertext and hypermedia
Part-of-speech tagging for Twitter: annotation, features, and experiments
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies: short papers - Volume 2
Random texts exhibit Zipf's-law-like word frequency distribution
IEEE Transactions on Information Theory
Can simple social copying heuristics explain tag popularity in a collaborative tagging system?
Proceedings of the 5th Annual ACM Web Science Conference
Religious Politicians and Creative Photographers: Automatic User Categorization in Twitter
SOCIALCOM '13 Proceedings of the 2013 International Conference on Social Computing
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One potential disadvantage of social tagging systems is that due to the lack of a centralized vocabulary, a crowd of users may never manage to reach a consensus on the description of resources (e.g., books, users or songs) on the Web. Yet, previous research has provided interesting evidence that the tag distributions of resources may become semantically stable over time as more and more users tag them. At the same time, previous work has raised an array of new questions such as: (i) How can we assess the semantic stability of social tagging systems in a robust and methodical way? (ii) Does semantic stabilization of tags vary across different social tagging systems and ultimately, (iii) what are the factors that can explain semantic stabilization in such systems? In this work we tackle these questions by (i) presenting a novel and robust method which overcomes a number of limitations in existing methods, (ii) empirically investigating semantic stabilization processes in a wide range of social tagging systems with distinct domains and properties and (iii) detecting potential causes for semantic stabilization, specifically imitation behavior, shared background knowledge and intrinsic properties of natural language. Our results show that tagging streams which are generated by a combination of imitation dynamics and shared background knowledge exhibit faster and higher semantic stability than tagging streams which are generated via imitation dynamics or natural language phenomena alone.