%0 Conference Paper %1 6425601 %A Sakakura, Y. %A Amagasa, T. %A Kitagawa, H. %B Advances in Social Networks Analysis and Mining (ASONAM), 2012 IEEE/ACM International Conference on %D 2012 %K related_work thesis %P 1153-1158 %R 10.1109/ASONAM.2012.199 %T Detecting Social Bookmark Spams Using Multiple User Accounts %U http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6425601 %X This paper proposes a scheme of detecting "Intensive Bookmarking using Multiple Accounts" (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.

@inproceedings{6425601, abstract = {This paper proposes a scheme of detecting "Intensive Bookmarking using Multiple Accounts" (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.}, added-at = {2015-03-24T18:54:09.000+0100}, author = {Sakakura, Y. and Amagasa, T. and Kitagawa, H.}, biburl = {https://www.bibsonomy.org/bibtex/2a7464cf5fb81b8fa6747fcadabc91a41/clemensbaier}, booktitle = {Advances in Social Networks Analysis and Mining (ASONAM), 2012 IEEE/ACM International Conference on}, description = {IEEE Xplore Abstract - Detecting Social Bookmark Spams Using Multiple User Accounts}, doi = {10.1109/ASONAM.2012.199}, interhash = {bcc53fdfed73137513af7ed4645bffc5}, intrahash = {a7464cf5fb81b8fa6747fcadabc91a41}, keywords = {related_work thesis}, month = aug, pages = {1153-1158}, timestamp = {2015-03-24T18:54:09.000+0100}, title = {Detecting Social Bookmark Spams Using Multiple User Accounts}, url = {http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6425601}, year = 2012 }