Post-Level Spam Detection for Social Bookmarking Web Sites

Abstract

Social book marking Web sites have emerged recently 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 manipulate them. One of the key features of the social book marking 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. Various techniques have been devised to tackle such tag spam detection problem. Most of these techniques were able to detect a user that always annotate spam tags. However, finer levels of detection are seldom discussed. In this work, we will propose a method based on a text mining approach to discover the relations between Web pages and there tag posts. These relations are then used to compute the similarity between a Web page and its tag post to decide if it is a spam post. Preliminary experiments show that the accuracy of the post-level spam detection task is 83%.