User review

User review

A user review is a review conducted by a computer user and published to a review site following product testing or the evaluation of a service. User reviews are commonly provided by consumers who volunteer to write the review, rather than professionals who are paid to evaluate the product or service. User reviews might be compared to professional nonprofit reviews from a consumer organization, or to promotional reviews from an advertiser or company marketing a product.


  • 1 Fake reviews
  • 2 Evaluation of user reviews
  • 3 Motivations for contributing a user review
  • 4 Case studies
  • 5 References

Fake reviews[edit]

Advertisers, marketers, and other stakeholders have motivation to produce fake positive user reviews for products they wish to promote or fake negative user reviews for products which they wish to disparage.[1] In a fake user review, an actor will create a user account based on some marketing persona and post a user review purporting to be a real person with the traits of the persona.[1] This is a misuse of the user review system, which universally only invite reviews from typical users and not paid fake personalities.[1]

One way to prevent fake reviews is to create barriers which favor long-term identified users who understand and support community rules in a review site.[1]

Amazon is suing fake reviewers.[2]

Evaluation of user reviews[edit]

Various systems have been proposed to evaluate the quality of user reviews so that consumers can access the best ones, avoid lower quality ones, and prevent mixing of honestly provided reviews with less honest reviews from advertisers or people with an agenda other than nonpartial evaluation.[3]

Consumers perceive user reviews using good grammar and persuasive writing style to be of higher quality than those written in other ways.[4]

The relationship between user reviews and the quality of a product is uncertain.[5] For some levels of quality in some circumstances, there may be no relationship between quality and ratings.[5] For top levels of quality, one study found that user ratings matched scientific ratings a little more than half the time.[5] Furthermore, people reading user reviews tend to perceive them to be as objective as scientific testing, especially when there is an average user review score.[6]

Given a large set of multiple user reviews by different people, there are text analytics algorithms which can accurately predict which reviews come from the same individual authors.[7]

Sentiment analysis can be used to predict the extent to which a review is favorable or critical.[8][9]

Motivations for contributing a user review[edit]

Uses and gratifications theory is a discipline which considers why anyone would volunteer time to create a user review.[10]

Review bombing is when user reviews are made en masse in order to more strongly influence the creator of a product or its sales, in response to an actual or perceived slight against the customers.

Case studies[edit]

Many researchers have profiled user reviews on Yelp.[11]

Research has shown that user reviews often influence consumer purchases in the hospitality industry.[12]

User reviews have created criticism and questioning of health care practices, when before the advent of user reviews, health care providers were rarely criticized or evaluated by users.[13]


  • ^ a b c d Ott, Myle; Cardie, Claire; Hancock, Jeff (2012). “Estimating the prevalence of deception in online review communities”: 201. arXiv:1204.2804. doi:10.1145/ cite.citation{font-style:inherit}.mw-parser-output q{quotes:”””””””‘””‘”}.mw-parser-output code.cs1-code{color:inherit;background:inherit;border:inherit;padding:inherit}.mw-parser-output .cs1-lock-free a{background:url(“//”)no-repeat;background-position:right .1em center}.mw-parser-output .cs1-lock-limited a,.mw-parser-output .cs1-lock-registration a{background:url(“//”)no-repeat;background-position:right .1em center}.mw-parser-output .cs1-lock-subscription a{background:url(“//”)no-repeat;background-position:right .1em center}.mw-parser-output .cs1-subscription,.mw-parser-output .cs1-registration{color:#555}.mw-parser-output .cs1-subscription span,.mw-parser-output .cs1-registration span{border-bottom:1px dotted;cursor:help}.mw-parser-output .cs1-hidden-error{display:none;font-size:100%}.mw-parser-output .cs1-visible-error{font-size:100%}.mw-parser-output .cs1-subscription,.mw-parser-output .cs1-registration,.mw-parser-output .cs1-format{font-size:95%}.mw-parser-output .cs1-kern-left,.mw-parser-output .cs1-kern-wl-left{padding-left:0.2em}.mw-parser-output .cs1-kern-right,.mw-parser-output .cs1-kern-wl-right{padding-right:0.2em}
  • ^ Eamonn Fingleton. “After Amazon’s Crackdown, Will The Fake Customer Review Industry Just Move Offshore?”. Forbes. Retrieved 13 December 2015.
  • ^ Lappas, Theodoros; Terzi, Evimaria (2011). “Toward a Fair Review-Management System”. 6912: 293–309. doi:10.1007/978-3-642-23783-6_19. ISSN 0302-9743.
  • ^ Otterbacher, Jahna (2011). “Being Heard in Review Communities: Communication Tactics and Review Prominence”. Journal of Computer-Mediated Communication. 16 (3): 424–444. doi:10.1111/j.1083-6101.2011.01549.x. ISSN 1083-6101.
  • ^ a b c de Langhe, Bart; Fernbach, Phil; Lichtenstein, Donald R. (4 July 2016). “High Online User Ratings Don’t Actually Mean You’re Getting a Quality Product”. Harvard Business Review. Retrieved 6 July 2016.
  • ^ de Langhe, Bart; Fernbach, Philip M.; Lichtenstein, Donald R. (April 2016). “Navigating by the Stars: Investigating the Actual and Perceived Validity of Online User Ratings”. Journal of Consumer Research. 42 (6): 817–833. doi:10.1093/jcr/ucv047.
  • ^ Almishari, Mishari; Tsudik, Gene (2012). “Exploring Linkability of User Reviews”. 7459: 307–324. doi:10.1007/978-3-642-33167-1_18. ISSN 0302-9743.
  • ^ Duan, Wenjing; Cao, Qing; Yu, Yang; Levy, Stuart (2013). “Mining Online User-Generated Content: Using Sentiment Analysis Technique to Study Hotel Service Quality”: 3119–3128. doi:10.1109/HICSS.2013.400.
  • ^ Yatani, Koji; Novati, Michael; Trusty, Andrew; Truong, Khai N. (2011). “Review spotlight”: 1541. doi:10.1145/1978942.1979167.
  • ^ Hicks, Amy; Comp, Stephen; Horovitz, Jeannie; Hovarter, Madeline; Miki, Maya; Bevan, Jennifer L. (2012). “Why people use An exploration of uses and gratifications”. Computers in Human Behavior. 28 (6): 2274–2279. doi:10.1016/j.chb.2012.06.034. ISSN 0747-5632.
  • ^ Luca, Michael (2011). “Reviews, Reputation, and Revenue: The Case of Yelp.Com”. doi:10.2139/ssrn.1928601. ISSN 1556-5068.
  • ^ Ong, Beng Soo (2012). “The Perceived Influence of User Reviews in the Hospitality Industry”. Journal of Hospitality Marketing & Management. 21 (5): 463–485. doi:10.1080/19368623.2012.626743. ISSN 1936-8623.
  • ^ Hardey, M. (2010). “Consuming Professions: User-review websites and health services”. Journal of Consumer Culture. 10 (1): 129–149. doi:10.1177/1469540509355023. ISSN 1469-5405.

  • Source:

    Product Review