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Detecting Product Review Spammers using Rating Behaviors

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

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Detecting Product Review Spammers using Rating Behaviors

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  1. Detecting Product Review Spammers using Rating Behaviors Presenter: Jun-Yi Wu Authors: Ee-Peng Lim, Viet-An Nguyen, Nitin Jindal, Bing Liu, HadyW.Lauw 國立雲林科技大學 National Yunlin University of Science and Technology 2010 CIKM

  2. Outline • Motivation • Objective • Methodology • Experiments • Conclusion • Comments

  3. 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.

  4. 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.

  5. Methodology • 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)

  6. Methodology • Target-base Spamming • Targeting Products • Rating Spamming • Review Text Spamming • Combined Spam Score

  7. Methodology • Target-base Spamming • Targeting Product Groups • Single Product Group Multiple High Ratings • Single Product Group Multiple Low Ratings • Combined Spam Score

  8. Methodology • Deviation-based Spamming • General Deviation • Early Deviation

  9. Experiments • 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

  10. Experiments • User Evaluation (unsupervised) • Evaluation Methodology • Review Spammer Evaluation software • Experiment Setup

  11. Experiments • Results • Inter-evaluator consistency • Spammer Ranking Performance

  12. Experiments • Results • Spammer Ranking Performance

  13. Experiments • Supervised spammer detection and analysis of spammed objects • Regression Model for Spammers

  14. Experiments • Analysis of Spammed Products and Product Groups

  15. Conclusion • 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 groupswill experience more significant changes in aggregate rating and reviewer count compared with removing randomly scored or unhelpful reviewers. 15

  16. Comments • Advantage • Many experiments • Application • Data Mining 16

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