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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 國國國國國國國國 National Yunlin University of Science and Technology

Detecting Product Review Spammers using Rating Behaviors

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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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Page 1: Detecting Product Review Spammers using Rating Behaviors

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

Page 2: Detecting Product Review Spammers using Rating Behaviors

Intelligent Database Systems Lab

N.Y.U.S.T.

I. M.Outline

Motivation Objective Methodology Experiments Conclusion Comments

2

Page 3: Detecting Product Review Spammers using Rating Behaviors

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.

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Page 4: Detecting Product Review Spammers using Rating Behaviors

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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Page 5: Detecting Product Review Spammers using Rating Behaviors

Intelligent Database Systems Lab

N.Y.U.S.T.

I. M.Methodology

5

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)

Page 6: Detecting Product Review Spammers using Rating Behaviors

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

Page 7: Detecting Product Review Spammers using Rating Behaviors

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

Page 8: Detecting Product Review Spammers using Rating Behaviors

Intelligent Database Systems Lab

N.Y.U.S.T.

I. M.Methodology

8

Deviation-based Spamming General Deviation

Early Deviation

Page 9: Detecting Product Review Spammers using Rating Behaviors

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

Page 10: Detecting Product Review Spammers using Rating Behaviors

Intelligent Database Systems Lab

N.Y.U.S.T.

I. M.Experiments

10

User Evaluation (unsupervised) Evaluation Methodology

Review Spammer Evaluation software

Experiment Setup

Page 11: Detecting Product Review Spammers using Rating Behaviors

Intelligent Database Systems Lab

N.Y.U.S.T.

I. M.Experiments

11

Results Inter-evaluator consistency

Spammer Ranking Performance

Page 12: Detecting Product Review Spammers using Rating Behaviors

Intelligent Database Systems Lab

N.Y.U.S.T.

I. M.Experiments

12

Results Spammer Ranking Performance

Page 13: Detecting Product Review Spammers using Rating Behaviors

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

Page 14: Detecting Product Review Spammers using Rating Behaviors

Intelligent Database Systems Lab

N.Y.U.S.T.

I. M.Experiments

14

Analysis of Spammed Products and Product Groups

Page 15: Detecting Product Review Spammers using Rating Behaviors

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.

Page 16: Detecting Product Review Spammers using Rating Behaviors

Intelligent Database Systems Lab

N.Y.U.S.T.

I. M.Comments

1616

Advantage Many experiments

Application Data Mining