Reporting incentives and biases in online review forums

Online reviews have become increasingly popular as a way to judge the quality of various products and services. However, recent work demonstrates that the absence of reporting incentives leads to a biased set of reviews that may not reflect the true quality. In this paper, we investigate underlying factors that influence users when reporting feedback. In particular, we study both reporting incentives and reporting biases observed in a widely used review forum, the Tripadvisor Web site. We consider three sources of information: first, the numerical ratings left by the user for different aspects of quality; second, the textual comment accompanying a review; third, the patterns in the time sequence of reports. We first show that groups of users who discuss a certain feature at length are more likely to agree in their ratings. Second, we show that users are more motivated to give feedback when they perceive a greater risk involved in a transaction. Third, a user's rating partly reflects the difference between true quality and prior expectation of quality, as inferred from previous reviews. We finally observe that because of these biases, when averaging review scores there are strong differences between the mean and the median. We speculate that the median may be a better way to summarize the ratings.

[1]  Paul A. Pavlou,et al.  Can online reviews reveal a product's true quality?: empirical findings and analytical modeling of Online word-of-mouth communication , 2006, EC '06.

[2]  Chrysanthos Dellarocas,et al.  Exploring the value of online product reviews in forecasting sales: The case of motion pictures , 2007 .

[3]  Bing Liu,et al.  Mining and summarizing customer reviews , 2004, KDD.

[4]  R. Olshavsky,et al.  Consumer Expectations, Product Performance, and Perceived Product Quality , 1972 .

[5]  Anindya Ghose,et al.  A Multi-Level Examination of the Impact of Social Identities on Economic Transactions in Electronic Markets , 2006 .

[6]  Boi Faltings,et al.  Understanding user behavior in online feedback reporting , 2007, EC '07.

[7]  M. Satterthwaite,et al.  Strategy-proofness and single-peakedness , 1976 .

[8]  Angelika Dimoka,et al.  The Nature and Role of Feedback Text Comments in Online Marketplaces: Implications for Trust Building, Price Premiums, and Seller Differentiation , 2006, Inf. Syst. Res..

[9]  A. Parasuraman,et al.  SERVQUAL: A multiple-item scale for measuring consumer perceptions of service quality. , 1988 .

[10]  Arun Sundararajan,et al.  Reputation premiums in electronic peer-to-peer markets: analyzing textual feedback and network structure , 2005, P2PECON '05.

[11]  Boi Faltings,et al.  Aggregating Reputation Feedback , 2009 .

[12]  H. Moulin On strategy-proofness and single peakedness , 1980 .

[13]  R. Teas,et al.  Expectations, Performance Evaluation, and Consumers’ Perceptions of Quality , 1993 .

[14]  Oren Etzioni,et al.  Extracting Product Features and Opinions from Reviews , 2005, HLT.

[15]  M. Melnik,et al.  Does a Seller's Ecommerce Reputation Matter? Evidence from Ebay Auctions , 2003 .

[16]  J. Wooders,et al.  Reputation in Auctions: Theory, and Evidence from Ebay , 2006 .

[17]  Anat R. Admati,et al.  Research Paper Series Graduate School of Business Stanford University Noisytalk.com: Broadcasting Opinions in a Noisy Environment Broadcasting Opinions in a Noisy Environment , 2022 .

[18]  Xin Li,et al.  Self-selection, slipping, salvaging, slacking, and stoning: the impacts of negative feedback at eBay , 2005, EC '05.

[19]  A. Parasuraman,et al.  A Conceptual Model of Service Quality and Its Implications for Future Research , 1985 .

[20]  Panagiotis G. Ipeirotis,et al.  The Dimensions of Reputation in Electronic Markets , 2009 .

[21]  Bo Pang,et al.  Thumbs up? Sentiment Classification using Machine Learning Techniques , 2002, EMNLP.

[22]  Vibhu O. Mittal,et al.  Comparative Experiments on Sentiment Classification for Online Product Reviews , 2006, AAAI.

[23]  S. McIntyre,et al.  Return on Reputation in Online Auction Markets , 2001 .

[24]  David M. Pennock,et al.  Mining the peanut gallery: opinion extraction and semantic classification of product reviews , 2003, WWW '03.