A large-scale study of link spam detection by graph algorithms
AIRWeb '07 Proceedings of the 3rd international workshop on Adversarial information retrieval on the web
Combating spam in tagging systems
AIRWeb '07 Proceedings of the 3rd international workshop on Adversarial information retrieval on the web
Fighting Spam on Social Web Sites: A Survey of Approaches and Future Challenges
IEEE Internet Computing
The dogear game: a social bookmark recommender system
Proceedings of the 2007 international ACM conference on Supporting group work
The anti-social tagger: detecting spam in social bookmarking systems
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
Towards improving web search by utilizing social bookmarks
ICWE'07 Proceedings of the 7th international conference on Web engineering
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This paper proposes a scheme of detecting 聛gIntensive Bookmarking using Multiple Accounts聛h (IBMA), where many social bookmark accounts are used to create bookmark entries linking to the target web resources with the aim of increasing site visitors or optimizing search result ranking. To efficiently detect IBMA, we propose to use clustering social bookmark user accounts according to the similarity with respect to the book marked web resources or web sites. Specifically, we cluster users who create bookmarks linking to similar set of web resources or web sites. For this, we propose three similarity measurements over two sets of bookmarks. We experimentally show that the proposed scheme successfully detects IBMA spammers in a real dataset. We also evaluate the accuracy of the proposed scheme with varying the similarity measurements, and characterize them.