A Tangled Web: Should Online Review Portals Display Fraudulent Reviews?

• 成果类型: Review
• 署名作者: Ananthakrishnan, Uttara M.; Li, Beibei; Smith, Michael D.
• 署名单位: University of Washington; University of Washington Seattle; Carnegie Mellon University
• 刊物名称: INFORMATION SYSTEMS RESEARCH
• ISSN/ISSBN: 1047-7047
• DOI: 10.1287/isre.2020.0925
• 发表日期: 2020
• 页码: 950-971
• 关键词: search engines; fake reviews; E-commerce; trust; sales; disclosure; ranking; IMPACT

摘要

The growing interest in online product reviews for legitimate promotion has been accompanied by an increase in fraudulent reviews. However, beyond algorithms for initial fraud detection, little is known about what review portals should do with fraudulent reviews after detecting them. In this paper, we address this question by studying how consumers respond to potentially fraudulent reviews and how review portals can leverage this knowledge to design better fraud management policies. To do this, we combine theoretical development from the trust literature with randomized experiments and statistical analysis using large-scale data from Yelp. We find that consumers tend to increase their trust in the information provided by review portals when the portal displays fraudulent reviews along with non-fraudulent reviews, as opposed to the common practice of censoring suspected fraudulent reviews. The impact of fraudulent reviews on consumers' decision-making process increases with the uncertainty in the initial evaluation of product quality. We also find that consumers do not effectively process the content of fraudulent reviews (negative or positive). This result furthers the case for a decision heuristic that will incorporate the motivational differences between positive fraudulent reviews and negative fraudulent reviews to effectively aid consumers' decision making.