Tagging systems allow users to interactively annotate a pool of shared resources using descriptive strings, which are called tags. Tags are used to guide users to interesting resources and help them build communities that share their expertise and resources. As tagging systems are gaining in popularity, they become more susceptible to tag spam: misleading tags that are generated in order to increase the visibility of some resources or simply to confuse users. Our goal is to understand this problem better. In particular, we are interested in answers to questions such as: How many malicious users can a tagging system tolerate before results significantly degrade? What types of tagging systems are more vulnerable to malicious attacks? What would be the effort and the impact of employing a trusted moderator to find bad postings? Can a system automatically protect itself from spam, for instance, by exploiting user tag patterns? In a quest for answers to these questions, we introduce a framework for modeling tagging systems and user tagging behavior. We also describe a method for ranking documents matching a tag based on taggers ’ reliability. Using our framework, we study the behavior of existing approaches under malicious attacks and the impact of a moderator and our ranking method. 1.

Social sites have become extremely popular among users but have they attracted equal attention from the research community? Are they good only for simple tasks, such as tagging and poking friends? Do they present any new or interesting research challenges? In this paper, we describe the insights we have obtained implementing CourseRank, a course evaluation and planning social system. We argue that more attention should be given to social sites like ours and that there are many challenges (though not the traditional DBMS ones) that should be addressed by our community. 1.

Social sites such as FaceBook, Orkut, Flickr, MySpace and many others have become immensely popular. At these sites, users share their resources (e.g., photos, profiles, blogs) and learn from each other. On the other hand, higher education applications help students and administrators track and manage academic information such as grades, course evaluations and enrollments. Despite the importance of both these areas, there is relatively little research on the mechanisms that make them effective. Apart from being both a successful social site and an academic planning site, CourseRank provides a live testbed for studying fundamental questions related to social networking, academic planning, and the fusion of these areas. In this paper, we provide a system overview and our main research efforts through CourseRank. 1.

It is popular for users in Web 2.0 era to freely annotate online resources with tags. To ease the annotation process, it has been great interest in automatic tag suggestion. We propose a method to suggest tags according to the text description of a resource. By considering both the description and tags of a given resource as summaries to the resource written in two languages, we adopt word alignment models in statistical machine translation to bridge their vocabulary gap. Based on the translation probabilities between the words in descriptions and the tags estimated on a large set of description-tags pairs, we build a word trigger method (WTM) to suggest tags according to the words in a resource description. Experiments on real world datasets show that WTM is effective and robust compared with other methods. Moreover, WTM is relatively simple and efficient, which is practical for Web applications. 1