Jessica Staddon
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.
- B. Adler and L. de Alfaro. A content-driven reputation system for the wikipedia. WWW 2007. Google Scholar
Digital Library
- R. Agrawal and R. Srikant. Fast algorithms for mining association rules. Proceedings of the 20th VLDB Conference, Santiago, Chile, 1994. Google ScholarDigital Library
- R. Agrawal, H. Mannila, R. Srikant, H. Toivonen and A. Verkamo. Fast discovery of association rules, Advances in Knowledge Discovery and Data Mining, AAAI/MIT Press, Cambridge, MA. Google ScholarDigital Library
- D. Bollegala, Y. Matsuo and M. Ishizuka. Measuring semanticsimilarity between words using Web search engines. WWW 2007. Google ScholarDigital Library
- J. Chevalier and D. Mayzlin. The Effect of Word of Mouth on Sales: Online Book Reviews. August 6, 2003. Yale SOM Working Paper No's. ES-28 & MK-15.Google Scholar
- R. Chow, P. Golle and J. Staddon. Detecting privacy leaks using corpus-based association rules. In ACM SIGKDD 2008. Google ScholarDigital Library
- P. Cimiano and S. Staab. Learning by Googling, ACM SIGKDD Explorations Newsletter, 2004. Google ScholarDigital Library
- comScore and The Kelsey Group. November 29,2007.Google Scholar
- M. Dowman, V. Tablan, H. Cunningham and B. Popov. Web-Assisted Annotation, Semantic Indexing and Search of Television and Radio News. WWW 2005. Google ScholarDigital Library
- eModeration. http://www.emoderation.comGoogle Scholar
- N. Glance. Community search assistant. The 6th International Conference on Intelligent User Interfaces, 2001. Google ScholarDigital Library
- R. Guha, R. Kumar, P. Raghavan and A. Tomkins. Propagation of trust and distrust. WWW 2004. Google ScholarDigital Library
- H. Lauw, E-P.Lim and K. Wang. Bias and controversy:beyond statistical deviation. KDD 2006. Google ScholarDigital Library
- V. Griffith. WikiScanner. http://virgil.grGoogle Scholar
- A. Gome. Web two point uh-oh! "It's my shout" Blog on Smart Company. March, 2007.Google Scholar
- Hakia. http://www.hakia.com/Google Scholar
- V. Mickunas. Amazon.com book reviewer shakeout. Dayton Daily News. April 14, 2007.Google Scholar
- . Mowshowitz and A. Kawaguchi. Bias on the Web.Communications of the ACM (CACM), September 2002. Google ScholarDigital Library
- A. Nakov and M. Hearst. Solving relational similarityproblems using the Web as a corpus. Proceedings of ACL/HLT2008.Google Scholar
- PowerReviews. Social Shopping Study 2007. http://www.powerreviews.comGoogle Scholar
- D. Quercia, S. Hailes and L. Capra. Lightweight distributed trust propagation. ICDM2007. Google ScholarDigital Library
- P. Resnick, R. Zeckhauser, E. Friedman, K. Kuwabara. Reputation Systems. Communications of the ACM, 2000. Google ScholarDigital Library
- T. Riggs and R. Wilensky. An algorithm for automatedrating of reviewers. JCDL 2001. Google ScholarDigital Library
- G. Sandoval. Digg continues to battle phony stories. CNET.com, December 18, 2006.Google Scholar
- P. Turney. Expressing implicit semantic relationswithout supervision. Proceedings of ACL 2006. Google ScholarDigital Library
- R. Verkaik. Google sued over defamatory posts found on Web search. The Independent. June, 2007.Google Scholar
- Yahoo! Search BOSS. http://developer.yahoo.com/search/boss/Google Scholar
Index Terms
- Detecting reviewer bias through web-based association mining
Recommendations
Mining learner profile utilizing association rule for web-based learning diagnosis
With the rapid growth of computer and Internet technologies, e-learning has become a major trend in the computer assisted teaching and learning fields. Most past researches for web-based learning focused on the issues of adaptive presentation, adaptive ...
Association Rule Mining in Fuzzy Political Donor Communities
Machine Learning and Data Mining in Pattern RecognitionAbstractSocial networks can be found in many domains. While community analysis can help users understand relationships within the network, it can be difficult to analyze the results without considerable effort. However, using the communities to partition ...
Analyzing Online Transaction Data using Association Rule Mining: Misumi Philippines Market Basket Analysis
ICIT '19: Proceedings of the 2019 7th International Conference on Information Technology: IoT and Smart CityAssociation Rule Mining is a data mining designed to discover the real connections of data items in transaction data build on associativity. The technique utilizes the Apriori Algorithm to discover association rules. Furthermore, the Apriori Algorithm ...
Comments