Publication Type

Conference Proceeding Article

Publication Date

10-2010

Abstract

This paper aims to detect users generating spam reviews or review spammers. We identify several characteristic be- haviors of review spammers and model these behaviors so as to detect the spammers. In particular, we seek to model the following behaviors. First, spammers may target specific products or product groups in order to maximize their im- pact. Second, they tend to deviate from the other reviewers in their ratings of products. We propose scoring methods to measure the degree of spam for each reviewer and apply them on an Amazon review dataset. We then select a sub- set of highly suspicious reviewers for further scrutiny by our user evaluators with the help of a web based spammer eval- uation software specially developed for user evaluation experiments. Our results show that our proposed ranking and supervised methods are e®ective in discovering spammers and outperform other baseline method based on helpfulness votes alone. We finally show that the detected spammers have more significant impact on ratings compared with the unhelpful reviewers.

Keywords

Algorithms, Measurement, Experimentation

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: October 26-30, 2010, Toronto

First Page

939

Last Page

948

ISBN

9781450300995

Identifier

10.1145/1871437.1871557

Publisher

ACM

City or Country

New York

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

Additional URL

http://dx.doi.org/10.1145/1871437.1871557

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