Authoritative sources in a hyperlinked environment
Journal of the ACM (JACM)
Usage patterns of collaborative tagging systems
Journal of Information Science
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Proceedings of the 15th international conference on World Wide Web
HT06, tagging paper, taxonomy, Flickr, academic article, to read
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tagging, communities, vocabulary, evolution
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Proceedings of the 16th international conference on World Wide Web
16th International World Wide Web Conference
The complex dynamics of collaborative tagging
Proceedings of the 16th international conference on World Wide Web
Optimizing web search using social annotations
Proceedings of the 16th international conference on World Wide Web
Combating spam in tagging systems
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Fighting Spam on Social Web Sites: A Survey of Approaches and Future Challenges
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Tag-aware recommender systems by fusion of collaborative filtering algorithms
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Collaborative tagging, supported by many social networking websites, is currently enjoying an increasing popularity. The usefulness of this largely available tag data has been explored in many applications including web resources categorization,deriving emergent semantics, web search etc. However, since tags are supplied by users freely , not all of them are useful and reliable, especially when they are generated by spammers with malicious intent. Therefore, identifying tags of high quality is crucial in improving the performance of applications based on tags. In this paper, we propose TRP-Rank (Tag-Resource Pair Rank), an algorithm to measure the quality of tags by manually assessing a seed set and propagating the quality through a graph. The three dimensional relationship among users, tags and web resources is firstly represented by a graph structure. A set of seed nodes, where each node represents a tag annotating a resource, is then selected and their quality is assessed. The quality of the remaining nodes is calculated by propagating the known quality of the seeds through the graph structure. We evaluate our approach on a public data set where tags generated by suspicious spammers were manually labelled. The experimental results demonstrate the effectiveness of this approach in measuring the quality of tags.