Detecting reviewer bias through web-based association mining

ABSTRACT

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.

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INDEX TERMS

Primary Classification:
  K. Computing Milieux
  K.6 MANAGEMENT OF COMPUTING AND INFORMATION SYSTEMS
      K.6.5 Security and Protection (D.4.6, K.4.2)

General Terms:
Security

Keywords:
association rule mining, bias, reputation, trust