The anti-social tagger: detecting spam in social bookmarking systems

The annotation of web sites in social bookmarking systems has become a popular way to manage and find information on the web. The community structure of such systems attracts spammers: recent post pages, popular pages or specific tag pages can be manipulated easily. As a result, searching or tracking recent posts does not deliver quality results annotated in the community, but rather unsolicited, often commercial, web sites. To retain the benefits of sharing one's web content, spam-fighting mechanisms that can face the flexible strategies of spammers need to be developed. A classical approach in machine learning is to determine relevant features that describe the system's users, train different classifiers with the selected features and choose the one with the most promising evaluation results. In this paper we will transfer this approach to a social bookmarking setting to identify spammers. We will present features considering the topological, semantic and profile-based inf...

• Type: Conference
• Year: 2008
• Where: AIRWEB
• Authors: Beate Krause, Christoph Schmitz, Andreas Hotho, Gerd Stumme
• Keywords: AIRWEB 2008, Internet Technology, Social Bookmarking, Social Bookmarking Systems, Web Sites