Usage patterns of collaborative tagging systems
Journal of Information Science
An introduction to ROC analysis
Pattern Recognition Letters - Special issue: ROC analysis in pattern recognition
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IEEE Internet Computing
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AI Communications - Network Analysis in Natural Sciences and Engineering
Characterizing privacy in online social networks
Proceedings of the first workshop on Online social networks
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AIRWeb '08 Proceedings of the 4th international workshop on Adversarial information retrieval on the web
Proceedings of the 5th International Workshop on Adversarial Information Retrieval on the Web
A brief survey on anonymization techniques for privacy preserving publishing of social network data
ACM SIGKDD Explorations Newsletter
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Proceedings of the fourth international conference on Communities and technologies
BNCOD 26 Proceedings of the 26th British National Conference on Databases: Dataspace: The Final Frontier
A co-classification framework for detecting web spam and spammers in social media web sites
Proceedings of the 18th ACM conference on Information and knowledge management
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Proceedings of the 2nd ACM workshop on Security and artificial intelligence
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Proceedings of the 23rd British HCI Group Annual Conference on People and Computers: Celebrating People and Technology
Class-based graph anonymization for social network data
Proceedings of the VLDB Endowment
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Proceedings of the 19th international conference on World wide web
Mining social media: key players, sentiments, and communities
Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery
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With the increased popularity of Web 2.0 services in the last years data privacy has become a major concern for users. The more personal data users reveal, the more difficult it becomes to control its disclosure in the web. However, for Web 2.0 service providers, the data provided by users is a valuable source for offering effective, personalised data mining services. One major application is the detection of spam in social bookmarking systems: in order to prevent a decrease of content quality, providers need to distinguish spammers and exclude them from the system. They thereby experience a conflict of interests: on the one hand, they need to identify spammers based on the information they collect about users, on the other hand, they need to respect privacy concerns and process as few personal data as possible. It would therefore be of tremendous help for system developers and users to know which personal data are needed for spam detection and which can be ignored. In this paper we address these questions by presenting a data privacy aware feature engineering approach. It consists of the design of features for spam classification which are evaluated according to both, performance and privacy conditions. Experiments using data from the social bookmarking system BibSonomy show that both conditions must not exclude each other.