Fast discovery of association rules
Advances in knowledge discovery and data mining
Communications of the ACM
Proceedings of the 6th international conference on Intelligent user interfaces
An algorithm for automated rating of reviewers
Proceedings of the 1st ACM/IEEE-CS joint conference on Digital libraries
Communications of the ACM
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Propagation of trust and distrust
Proceedings of the 13th international conference on World Wide Web
ACM SIGKDD Explorations Newsletter
Web-assisted annotation, semantic indexing and search of television and radio news
WWW '05 Proceedings of the 14th international conference on World Wide Web
Bias and controversy: beyond the statistical deviation
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Expressing implicit semantic relations without supervision
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
A content-driven reputation system for the wikipedia
Proceedings of the 16th international conference on World Wide Web
Measuring semantic similarity between words using web search engines
Proceedings of the 16th international conference on World Wide Web
Detecting privacy leaks using corpus-based association rules
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Lightweight Distributed Trust Propagation
ICDM '07 Proceedings of the 2007 Seventh IEEE International Conference on Data Mining
Distortion as a validation criterion in the identification of suspicious reviews
Proceedings of the First Workshop on Social Media Analytics
Estimating sequential bias in online reviews: A Kalman filtering approach
Knowledge-Based Systems
Input online review data and related bias in recommender systems
Decision Support Systems
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Online retailers and content distributors benefit from an active community that shares credible reviews and recommendations. Today, the most popular approach to encouraging credibility in these communities is self-regulation; community members rate reviews according to their accuracy and usefulness, thus helping to weed out reviews that are inaccurate. This self-regulation, while powerful, is limited by its insularity. Community members generally base their assessments on a reviewer's comments and actions only within the community. This ignores relationships the reviewer has outside the community that may be quite relevant to evaluating the reviewer's comments; for example, a relationship between an author and reviewer. We present a simple method for mining the Web to detect many such associations. Our method, together with self-regulation, provides for more comprehensive detection of bias in reviews by alerting the user to the potential for an undisclosed relationship between a reviewer and author. We provide preliminary results using book reviews in Amazon.com demonstrating that our approach is a high-precision method for detecting strong relationships between reviewers and authors that may contribute to reviewer bias.