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Fraud detection in online consumer reviews

  • Nan Hu
  • , Ling Liu
  • , Vallabh Sambamurthy
  • University of Wisconsin-Eau Claire
  • Singapore Management University
  • Michigan State University

Research output: Contribution to journalArticlepeer-review

163 Scopus citations

Abstract

Increasingly, consumers depend on social information channels, such as user-posted online reviews, to make purchase decisions. These reviews are assumed to be unbiased reflections of other consumers' experiences with the products or services. While extensively assumed, the literature has not tested the existence or non-existence of review manipulation. By using data from Amazon and Barnes & Noble, our study investigates if vendors, publishers, and writers consistently manipulate online consumer reviews. We document the existence of online review manipulation and show that the manipulation strategy of firms seems to be a monotonically decreasing function of the product's true quality or the mean consumer rating of that product. Hence, manipulation decreases the informativeness of online reviews. Furthermore though consumers understand the existence of manipulation, they can only partially correct it based on their expectation of the overall level of manipulation. Hence, vendors are able to change the final outcomes by manipulating online reviewers. In addition, we demonstrate that at the early stages, after an item is released to the Amazon market, both price and reviews serve as quality indicators. Thus, at this stage, a higher price leads to an increase in sales instead of a decrease in sales. At the late stages, price assumes its normal role, meaning a higher price leads to a decrease in sales. Finally, on average, there is a higher level of manipulation on Barnes & Noble than on Amazon.

Original languageEnglish
Pages (from-to)614-626
Number of pages13
JournalDecision Support Systems
Volume50
Issue number3
DOIs
StatePublished - Feb 2011
Externally publishedYes

Keywords

  • Manipulation
  • Online word of mouth
  • Price
  • Self-selection
  • Time-series

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