Communications of the ACM
Collection statistics for fast duplicate document detection
ACM Transactions on Information Systems (TOIS)
On the Resemblance and Containment of Documents
SEQUENCES '97 Proceedings of the Compression and Complexity of Sequences 1997
Volunteer computing
"In vivo" spam filtering: a challenge problem for KDD
ACM SIGKDD Explorations Newsletter
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
The Knowledge Engineering Review
Fighting Spam with Reputation Systems
Queue - Social Computing
The challenges of service-side personalized spam filtering: scalability and beyond
InfoScale '06 Proceedings of the 1st international conference on Scalable information systems
Finding near-duplicate web pages: a large-scale evaluation of algorithms
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Relaxed online SVMs for spam filtering
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Support vector machines for spam categorization
IEEE Transactions on Neural Networks
Large human communication networks: patterns and a utility-driven generator
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Monitoring algorithms for negative feedback systems
Proceedings of the 19th international conference on World wide web
Social network analysis of web links to eliminate false positives in collaborative anti-spam systems
Journal of Network and Computer Applications
Text mining and probabilistic language modeling for online review spam detection
ACM Transactions on Management Information Systems (TMIS)
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Spam is a growing problem; it interferes with valid email and burdens both email users and service providers. In this work, we propose a reactive spam-filtering system based on reporter reputation for use in conjunction with existing spam-filtering techniques. The system has a trust-maintenance component for users, based on their spam-reporting behavior. The challenge that we consider is that of maintaining a reliable system, not vulnerable to malicious users, that will provide early spam-campaign detection to reduce the costs incurred by users and systems. We report on the utility of a reputation system for spam filtering that makes use of the feedback of trustworthy users. We evaluate our proposed framework, using actual complaint feedback from a large population of users, and validate its spam-filtering performance on a collection of real email traffic over several weeks. To test the broader implication of the system, we create a model of the behavior of malicious reporters, and we simulate the system under various assumptions using a synthetic dataset.