A content-driven reputation system for the wikipedia
Proceedings of the 16th international conference on World Wide Web
Network analysis of collaboration structure in Wikipedia
Proceedings of the 18th international conference on World wide web
User generated content: how good is it?
Proceedings of the 3rd workshop on Information credibility on the web
Proceedings of the 9th ACM/IEEE-CS joint conference on Digital libraries
Identifying featured articles in wikipedia: writing style matters
Proceedings of the 19th international conference on World wide web
Measuring author contributions to the Wikipedia
WikiSym '08 Proceedings of the 4th International Symposium on Wikis
Co-authorship 2.0: patterns of collaboration in Wikipedia
Proceedings of the 22nd ACM conference on Hypertext and hypermedia
Overview of the third international workshop on search and mining user-generated contents
Proceedings of the 20th ACM international conference on Information and knowledge management
Querying subgraph sets with g-tries
DBSocial '12 Proceedings of the 2nd ACM SIGMOD Workshop on Databases and Social Networks
Classifying Wikipedia articles using network motif counts and ratios
Proceedings of the Eighth Annual International Symposium on Wikis and Open Collaboration
Temporal analysis of activity patterns of editors in collaborative mapping project of OpenStreetMap
Proceedings of the 9th International Symposium on Open Collaboration
Towards a faster network-centric subgraph census
Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
Improving Wiki Article Quality Through Crowd Coordination: A Resource Allocation Approach
International Journal on Semantic Web & Information Systems
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Good Wikipedia articles are authoritative sources due to the collaboration of a number of knowledgeable contributors. This is the many eyes idea. The edit network associated with a Wikipedia article can tell us something about its quality or authoritativeness. In this paper we explore the hypothesis that the characteristics of this edit network are predictive of the quality of the corresponding article's content. We characterize the edit network using a profile of network motifs and we show that this network motif profile is predictive of the Wikipedia quality classes assigned to articles by Wikipedia editors. We further show that the network motif profile can identify outlier articles particularly in the 'Featured Article' class, the highest Wikipedia quality class.