Propagation of trust and distrust
Proceedings of the 13th international conference on World Wide Web
Usage patterns of collaborative tagging systems
Journal of Information Science
Topical TrustRank: using topicality to combat web spam
Proceedings of the 15th international conference on World Wide Web
Improved annotation of the blogosphere via autotagging and hierarchical clustering
Proceedings of the 15th international conference on World Wide Web
AutoTag: a collaborative approach to automated tag assignment for weblog posts
Proceedings of the 15th international conference on World Wide Web
HT06, tagging paper, taxonomy, Flickr, academic article, to read
Proceedings of the seventeenth conference on Hypertext and hypermedia
Structure and evolution of online social networks
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Link spam detection based on mass estimation
VLDB '06 Proceedings of the 32nd international conference on Very large data bases
tagging, communities, vocabulary, evolution
CSCW '06 Proceedings of the 2006 20th anniversary conference on Computer supported cooperative work
Combating spam in tagging systems
AIRWeb '07 Proceedings of the 3rd international workshop on Adversarial information retrieval on the web
Combating web spam with trustrank
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
A co-classification framework for detecting web spam and spammers in social media web sites
Proceedings of the 18th ACM conference on Information and knowledge management
Detecting tag spam in social tagging systems with collaborative knowledge
FSKD'09 Proceedings of the 6th international conference on Fuzzy systems and knowledge discovery - Volume 7
Survey on social tagging techniques
ACM SIGKDD Explorations Newsletter
Detecting spam blogs from blog search results
Information Processing and Management: an International Journal
Foundations and Trends in Information Retrieval
Associative tag recommendation exploiting multiple textual features
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
Spam or ham?: characterizing and detecting fraudulent "not spam" reports in web mail systems
Proceedings of the 8th Annual Collaboration, Electronic messaging, Anti-Abuse and Spam Conference
Can user tagging help health information seekers?
UAHCI'11 Proceedings of the 6th international conference on Universal access in human-computer interaction: applications and services - Volume Part IV
Improving neighborhood based Collaborative Filtering via integrated folksonomy information
Pattern Recognition Letters
Bursty event detection from collaborative tags
World Wide Web
Detecting collective attention spam
Proceedings of the 2nd Joint WICOW/AIRWeb Workshop on Web Quality
Uses of explicit and implicit tags in social bookmarking
Journal of the American Society for Information Science and Technology
Predicting semantic annotations on the real-time web
Proceedings of the 23rd ACM conference on Hypertext and social media
Tag-aware recommender systems: a state-of-the-art survey
Journal of Computer Science and Technology - Special issue on Community Analysis and Information Recommendation
Topic based photo set retrieval using user annotated tags
Multimedia Tools and Applications
Campaign extraction from social media
ACM Transactions on Intelligent Systems and Technology (TIST) - Special Section on Intelligent Mobile Knowledge Discovery and Management Systems and Special Issue on Social Web Mining
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Tagging systems allow users to interactively annotate a pool of shared resources using descriptive strings 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.