![]() ![]() tagging, communities, vocabulary, evolution. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA, 1993. Detecting spam web pages through content analysis. Blocking blog spam with language model disagreement. Ontologies are us: A unified model of social networks and semantics. Folksonomies - Cooperative Classification and Communication Through Shared Metadata, December 2004. Social Bookmarking Tools (II): A Case Study - Connotea. Lecture Notes in Computer Science, 3993:1114, Dec 2005. Collaborative tagging as a tripartite network. Detecting Spam Blogs: A Machine Learning Approach. AAAI Spring Symposium on Computational Approaches to Analyzing Weblogs, 2006. SVMs for the Blogosphere: Blog Identification and Splog Detection. Information retrieval in folksonomies: Search and ranking. In CS-TIW '06, Aalborg, Denmark, July 2006. BibSonomy: A social bookmark and publication sharing system. ![]() Fighting spam on social web sites: A survey of approaches and future challenges. ![]() Social Bookmarking Tools (I): A General Review. The structure of collaborative tagging systems. Improving web spam classifiers using link structure (s). Libsvm: a library for support vector machines (version 2.31). Know your neighbors: web spam detection using the web topology. Our results represent the groundwork for a first application in BibSonomy and for the building of more elaborate spam detection mechanisms. Based on our features, we will learn a large set of different classification models and compare their performance. The dataset used is a snapshot of the social bookmarking system BibSonomy and was built over the course of several months when cleaning the system from spam. We will present features considering the topological, semantic and profile-based information which people make public when using the system. In this paper we will transfer this approach to a social bookmarking setting to identify spammers. 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.Ī 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. As a result, searching or tracking recent posts does not deliver quality results annotated in the community, but rather unsolicited, often commercial, web sites. The community structure of such systems attracts spammers: recent post pages, popular pages or specific tag pages can be manipulated easily. The annotation of web sites in social bookmarking systems has become a popular way to manage and find information on the web. ![]()
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |