Title

Finding Unusual Review Patterns Using Unexpected Rules

Publication Type

Conference Proceeding Article

Publication Date

10-2010

Abstract

In recent years, opinion mining attracted a great deal of research attention. However, limited work has been done on detecting opinion spam (or fake reviews). The problem is analogous to spam in Web search [1, 9 11]. However, review spam is harder to detect because it is very hard, if not impossible, to recognize fake reviews by manually reading them [2]. This paper deals with a restricted problem, i.e., identifying unusual review patterns which can represent suspicious behaviors of reviewers. We formulate the problem as finding unexpected rules. The technique is domain independent. Using the technique, we analyzed an Amazon.com review dataset and found many unexpected rules and rule groups which indicate spam activities.

Keywords

Reviewer behavior, review spam, unexpected patterns

Discipline

Databases and Information Systems | Numerical Analysis and Scientific Computing

Research Areas

Data Management and Analytics

Publication

CIKM '10: Proceedings of the 19th ACM international conference on Information and knowledge management

First Page

1549

Last Page

1552

ISBN

9781450300995

Identifier

10.1145/1871437.1871669

Publisher

ACM

City or Country

Toronto

Additional URL

http://dx.doi.org/10.1145/1871437.1871669

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