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Detecting Product Review Spammers using Rating Behaviors. Presenter: Jun-Yi Wu Authors: Ee-Peng Lim, Viet-An Nguyen, Nitin Jindal, Bing Liu, Hady W.Lauw. 國立雲林科技大學 National Yunlin University of Science and Technology. 2010 CIKM. Outline. Motivation Objective Methodology Experiments - PowerPoint PPT Presentation
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Intelligent Database Systems Lab
N.Y.U.S.T.
I. M.
Detecting Product Review Spammers using Rating Behaviors
Presenter: Jun-Yi Wu Authors: Ee-Peng Lim, Viet-An Nguyen, Nitin Jindal, Bing Liu,
Hady W.Lauw
2010 CIKM
國立雲林科技大學National Yunlin University of Science and Technology
Intelligent Database Systems Lab
N.Y.U.S.T.
I. M.Outline
Motivation Objective Methodology Experiments Conclusion Comments
2
Intelligent Database Systems Lab
N.Y.U.S.T.
I. M.Motivation
Review spam is designed to give unfair view of some products so as to influence the consumer’s perception of the products by directly or indirectly inflating or damaging the product’s reputation.
3
Intelligent Database Systems Lab
N.Y.U.S.T.
I. M.Objective
To detect users generating spam reviews or review spammers.
To identify several characteristic behaviors of review spammers and model these behaviors so as to detect the spammers.
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Intelligent Database Systems Lab
N.Y.U.S.T.
I. M.Methodology
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Target-base Spamming Targeting Products (TP)
Rating Spamming Review Text Spamming Combined Spam Score
Targeting Product Groups (TG) Single Product Group Multiple High Ratings Single Product Group Multiple Low Ratings Combined Spam Score
Deviation-based Spamming General Deviation (GD) Early Deviation (ED)
Intelligent Database Systems Lab
N.Y.U.S.T.
I. M.Methodology
6
Target-base Spamming Targeting Products
Rating Spamming
Review Text Spamming
Combined Spam Score
Intelligent Database Systems Lab
N.Y.U.S.T.
I. M.Methodology
7
Target-base Spamming Targeting Product Groups
Single Product Group Multiple High Ratings
Single Product Group Multiple Low Ratings
Combined Spam Score
Intelligent Database Systems Lab
N.Y.U.S.T.
I. M.Methodology
8
Deviation-based Spamming General Deviation
Early Deviation
Intelligent Database Systems Lab
N.Y.U.S.T.
I. M.Experiments
9
User Evaluation (unsupervised) Objectives Evaluation Methodology
Review Spammer Evaluation software Experiment Setup
Results
Supervised Spammer Detection and Analysis of Spammed Objects (supervised ) Regression Model for Spammers Analysis of Spammed Products and Product Groups
Intelligent Database Systems Lab
N.Y.U.S.T.
I. M.Experiments
10
User Evaluation (unsupervised) Evaluation Methodology
Review Spammer Evaluation software
Experiment Setup
Intelligent Database Systems Lab
N.Y.U.S.T.
I. M.Experiments
11
Results Inter-evaluator consistency
Spammer Ranking Performance
Intelligent Database Systems Lab
N.Y.U.S.T.
I. M.Experiments
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Results Spammer Ranking Performance
Intelligent Database Systems Lab
N.Y.U.S.T.
I. M.Experiments
13
Supervised spammer detection and analysis of spammed objects Regression Model for Spammers
Intelligent Database Systems Lab
N.Y.U.S.T.
I. M.Experiments
14
Analysis of Spammed Products and Product Groups
Intelligent Database Systems Lab
N.Y.U.S.T.
I. M.Conclusion
1515
This paper proposes a behavioral approach to detect review spammers who try to manipulate review ratings on some target products or product groups.
To remove the highly spammed products and product groups will experience more significant changes in aggregate rating and reviewer count compared with removing randomly scored or unhelpful reviewers.
Intelligent Database Systems Lab
N.Y.U.S.T.
I. M.Comments
1616
Advantage Many experiments
Application Data Mining