Spam Detection In Social Bookmarking System – Empirical Evaluation

Abstract

Social Bookmarking web sites have recently become popular for collecting and sharing of interesting web sites among users. People can add web pages to such sites, as bookmarks and allow themselves as well as others to work on them. One of the key features of the social bookmarking sites is the ability of annotating a web page when it is being bookmarked. The annotation usually contains a set of words or phrases, which are collectively known as tags that could reveal the semantics of the annotated web page. Efficient and effective search of web pages can then be achieved via such tags. However, spam tags that are irrelevant to the content of web pages often appear to deceive other users for malicious or commercial purposes. There are users who intentionally assign spam tags. Manual detection of such users is very difficult.In this paper, main focus is on the detection of spam users in Social Bookmarking System. Experimental Evaluation is done using ECM PKDD discovery challenge 2008 data set. Experimentation using naïve Bayes and K-Nearest Neighbour classifiers on all three Information Retrieval (IR) models (Boolean, bag-ofwords and TFIDF) gives promising results.