Publication Type
Conference Proceeding Article
Version
submittedVersion
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
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
Citation
JINDAL, Nitin; LIU, Bing; and LIM, Ee Peng.
Finding unusual review patterns using unexpected rules. (2010). CIKM '10: Proceedings of the 19th ACM international conference on Information and knowledge management. 1549-1552.
Available at: https://ink.library.smu.edu.sg/sis_research/625
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
http://doi.org/10.1145/1871437.1871669
Included in
Databases and Information Systems Commons, Numerical Analysis and Scientific Computing Commons