BibSonomy publications for /user/hotho/spamFri Jan 20 09:29:21 CET 2012New York, NY, USAProceedings of the 11th International Conference on Knowledge Management and Knowledge Technologies15:1--15:8i-KNOW '11Privacy-aware spam detection in social bookmarking systems20112011 datamining detection myown privacy spam web20 With the increased popularity of Web 2.0 services in the last years data privacy has become a major concern for users. The more personal data users reveal, the more difficult it becomes to control its disclosure in the web. However, for Web 2.0 service providers, the data provided by users is a valuable source for offering effective, personalised data mining services. One major application is the detection of spam in social bookmarking systems: in order to prevent a decrease of content quality, providers need to distinguish spammers and exclude them from the system. They thereby experience a conflict of interests: on the one hand, they need to identify spammers based on the information they collect about users, on the other hand, they need to respect privacy concerns and process as few personal data as possible. It would therefore be of tremendous help for system developers and users to know which personal data are needed for spam detection and which can be ignored. In this paper we address these questions by presenting a data privacy aware feature engineering approach. It consists of the design of features for spam classification which are evaluated according to both, performance and privacy conditions. Experiments using data from the social bookmarking system BibSonomy show that both conditions must not exclude each other.Wed Oct 27 21:31:44 CEST 2010TREC 2006 Blog Track Notebook{Blog track open task: Spam blog classification}2006blog spam trec Thu May 20 20:53:45 CEST 2010Krakow, Poland7th Conference on Computer Methods and SystemsNovemberISBN 83-916420-5-4Who are the Spammers - Understandable Local Patterns for Concept Description20092009 bibsonomy discovery dm myown spam subgroup Sun Oct 25 13:29:41 CET 2009Proc. LeGo-09: From Local Patterns to Global Models, Workshop at the 2009 European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databasesaccepted{Towards Understanding Spammers - Discovering Local Patterns for Concept Characterization and Description}20092009 bibsonomy discovery dm myown spam subgroup Wed Aug 19 23:48:11 CEST 2009IEEE Internet Computing636-45Fighting Spam on Social Web Sites: A Survey of Approaches and Future Challenges.112007bookmarking social spam summerschool tagging taggingsurvey Wed Aug 19 23:47:36 CEST 2009AIRWebconf/airweb/200941-48ACM International Conference Proceeding SeriesSocial spam detection.2009bookmarking social spam summerschool tagging taggingsurvey Wed Aug 19 23:45:52 CEST 2009New York, NY, USAAIRWeb '07: Proceedings of the 3rd international workshop on Adversarial information retrieval on the web57--64Combating spam in tagging systems2007bookmarking folksonomy social spam summerschool tagging taggingsurvey Fri May 15 17:30:12 CEST 2009New York, NY, USAAIRWeb '08: Proceedings of the 4th international workshop on Adversarial information retrieval on the web61--68The Anti-Social Tagger - Detecting Spam in Social Bookmarking Systems20082008 bookmarking classification dm folksonomy mining ml myown social spam Tue Mar 13 17:57:29 CET 2007CEASconf/ceas/2005Scalable and Reliable Collaborative Spam Filters: Harnessing the Global Social Email Networks.2005email network social spam Tue Mar 13 17:56:43 CET 2007Reputation Network Analysis for Email Filtering2004email network spam Tue Mar 13 17:55:17 CET 2007Comparative Graph Theoretical Characterization of Networks of Spam and Legitimate Email2005email graph network spam Email is an increasingly important and ubiquitous means of communication, both facilitating contact between private individuals and enabling rises in the productivity of organizations. However the relentless rise of automatic unauthorized emails, a.k.a. spam is eroding away much of the attractiveness of email communication. Most of the attention dedicated to date to spam detection has focused on the content of the emails or on the addresses or domains associated with spam senders. Although methods based on these - easily changeable - identifiers work reasonably well they miss on the fundamental nature of spam as an opportunistic relationship, very different from the normal mutual relations between senders and recipients of legitimate email. Here we present a comprehensive graph theoretical analysis of email traffic that captures these properties quantitatively. We identify several simple metrics that serve both to distinguish between spam and legitimate email and to provide a statistical basis for models of spam traffic.Mon Mar 12 13:56:01 CET 2007New York, NY, USADocEng '06: Proceedings of the 2006 ACM symposium on Document engineering107--114Content based SMS spam filtering2006detection sms spam Wed Oct 25 14:18:48 CEST 2006Link spam detection based on mass estimation2005pagerank spam detection Fri Apr 21 15:58:58 CEST 2006ECML96-107Thwarting the Nigritude Ultramarine: Learning to Identify Link Spam.2005spam rank link mining ml Mon Apr 17 20:48:53 CEST 2006Palo Alto, CASecond Conference on Email and Anti-Spam (CEAS)Good Word Attacks on Statistical Spam Filters20052006 lecture spam email kdd application Mon Apr 17 20:41:55 CEST 2006February{Personal Email Networks: An Effective Anti-Spam Tool}20042006 lecture spam email kdd application Mon Feb 13 09:05:54 CET 2006VLDB576-587Combating Web Spam with TrustRank.2004network rank search social spam summerschool trust web