Intelligent Database Systems Lab N.Y.U.S.T. I. M. Fraud detection in online consumer reviews Presenter: Tsai Tzung Ruei Authors: Nan Hu, Ling Liu, Vallabh.

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Presentation transcript:

Intelligent Database Systems Lab N.Y.U.S.T. I. M. Fraud detection in online consumer reviews Presenter: Tsai Tzung Ruei Authors: Nan Hu, Ling Liu, Vallabh Sambamurthy DSS 2010 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 3 Buy ?

Intelligent Database Systems Lab N.Y.U.S.T. I. M. Objective To study temporal behaviors of online reviews and address the following research questions 4 Online reviews are written by actual previous customers and not publishers or vendors. Even if there is manipulation, consumers are smart and can adjust their interpretation of online opinions accordingly

Intelligent Database Systems Lab N.Y.U.S.T. I. M. Methodology 5 Amazon data Amazon data Regression Answer Hypotheses DEFINITION  Review fraud Review fraud as occurring when online vendors, publishers, or authors write “consumer” reviews by posing as real customers.  Order To represent the relative time. DEFINITION  Review fraud Review fraud as occurring when online vendors, publishers, or authors write “consumer” reviews by posing as real customers.  Order To represent the relative time.

Intelligent Database Systems Lab N.Y.U.S.T. I. M. Methodology How do consumer reviews evolve over time? 6 Amazon data Amazon data

Intelligent Database Systems Lab N.Y.U.S.T. I. M. Methodology Why do we suspect that there might be review fraud? 7 Amazon data Amazon data

Intelligent Database Systems Lab N.Y.U.S.T. I. M. Experiments A pure self-selection process or a combination of self- selection with manipulation? 8

Intelligent Database Systems Lab N.Y.U.S.T. I. M. Experiments The empirical test and robustness check 9

Intelligent Database Systems Lab N.Y.U.S.T. I. M. Experiments 10 Relation between quality and manipulation

Intelligent Database Systems Lab N.Y.U.S.T. I. M. Experiments Are consumers able to fully account for bias? customer original

Intelligent Database Systems Lab N.Y.U.S.T. I. M. Experiments How do customers make purchase decisions when manipulation exists? 12 H3. The price and the product sales will move in the same way when the manipulation is present; in the absence of manipulation, an increase in price will lead to a decrease in sales. H3. The price and the product sales will move in the same way when the manipulation is present; in the absence of manipulation, an increase in price will lead to a decrease in sales.

Intelligent Database Systems Lab N.Y.U.S.T. I. M. Experiments Manipulation across websites 13

Intelligent Database Systems Lab N.Y.U.S.T. I. M. Conclusion Future researches  To find a way to increase the cost of manipulation in order to mitigate the manipulation effect. Contribution  To detect fraud in online consumer reviews in many different case. 14

Intelligent Database Systems Lab N.Y.U.S.T. I. M. Comments Advantage  Multivariate analysis Drawback  Some mistakes Application  CRM 15