Social Bookmarking systems enable users to store, organize and search their resources. Furthermore, a social bookmarking system allows users to share their resources with others and even join groups of people with similar interests. The data size in social bookmarking systems has been increased sharply in recent years with the usage of such systems. However, such systems attract spammers due to their ease of use and popularity. Spammers have started misleading search engines and other bookmarking system users in order to direct web traffic towards their own pages. Strong prevention and detection methods in social bookmarking systems are indispensable in order to stop spam activities and guaranty the accuracy and reliability of information. In this paper, we introduce a novel framework for spam detection task in social bookmarking systems. Here, we propose a set of new features to improve the accuracy of spammer detection. Our experiments show that our features demonstrate a high discriminative power. A performance evaluation of our proposed method over different spammer detection methods indicate that the proposed framework yields an improvement of the prediction accuracy.