“Not Some Trumped Up Beef”: Assessing Credibility of Online Restaurant Reviews

  • Marina Kobayashi
  • Victoria Schwanda SosikEmail author
  • David Huffaker
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9298)


Online reviews, or electronic word of mouth (eWOM), are an essential source of information for people making decisions about products and services, however they are also susceptible to abuses such as spamming and defamation. Therefore when making decisions, readers must determine if reviews are credible. Yet relatively little research has investigated how people make credibility judgments of online reviews. This paper presents quantitative and qualitative results from a survey of 1,979 respondents, showing that attributes of the reviewer and review content influence credibility ratings. Especially important for judging credibility is the level of detail in the review, whether or not it is balanced in sentiment, and whether the reviewer demonstrates expertise. Our findings contribute to the understanding of how people judge eWOM credibility, and we suggest how eWOM platforms can be designed to coach reviewers to write better reviews and present reviews in a manner that facilitates credibility judgments.


eWOM Online review credibility Online review platforms 



We would like to thank Judy Zhao and Samantha Sachs for their work designing and writing the review stimuli used in this study. We would also like to thank Jeff Tamer for his help with data collection.


  1. 1.
    Arndt, J.: Role of product-related conversations in the diffusion of a new product. J. Mark. Res. 4, 291–295 (1967)CrossRefGoogle Scholar
  2. 2.
    Banning, S.A., Sweetser, K.D.: How much do they think it affects them and whom do they believe? Comparing the third-person effect and credibility of blogs and traditional media. Commun. Q. 55, 451–466 (2007)CrossRefGoogle Scholar
  3. 3.
    Bansal, H.S., Voyer, P.A.: Word-of-mouth processes within a service purchase decision context. J. Serv. Res. 3(2), 166–177 (2000)CrossRefGoogle Scholar
  4. 4.
    Bart, Y., Shankar, V., Sultan, F., Urban, G.L.: Are the drivers and role of online trust the same for all web sites and consumers? a large-scale exploratory empirical study. J. Mark. 69, 133–152 (2005)CrossRefGoogle Scholar
  5. 5.
    Brown, J.J., Reingen, P.H.: Social ties and word-of-mouth referral behavior. J. Consum. Res. 14(3), 350–362 (1987)CrossRefGoogle Scholar
  6. 6.
    Buhrmester, M., Kwang, T., Gosling, S.D.: Amazon’s mechanical turk a new source of inexpensive, yet high-quality, data?. Perspect. Psychol. Sci. 6(1), 3–5 (2011)CrossRefGoogle Scholar
  7. 7.
    Callister Jr., T.A.: Media literacy: on-ramp to the literacy of the 21st century or cul-de-sac on the information superhighway. Adv. Reading/Lang. Res. 7, 403–420 (2000)Google Scholar
  8. 8.
    Castillo, C., Mendoza, M., Poblete, B.: Information credibility on twitter. In: Proceedings of WWW 2011, pp. 675–684 (2011)Google Scholar
  9. 9.
    Dichter, E.: How word of mouth advertising works. Harvard Bus. Rev. 44(6), 147–160 (1966)Google Scholar
  10. 10.
    Doh, S.-J., Hwang, J.-S.: How Consumers Evaluate eWOM (Electronic Word-of-Mouth) Messages. CyberPsychol. Behav. 12(2), 193–197 (2009)CrossRefGoogle Scholar
  11. 11.
    Engel, J.F., Blackwell, R.D., Miniard, P.W.: Consumer Behavior, 8th edn. Dryden Press, Fort Worth (1993)Google Scholar
  12. 12.
    Fitch, J.W., Cromwell, R.L.: Evaluating internet resources: identity, affiliation, and cognitive authority in a networked world. J. Am. Soc. Inform. Sci. Technol. 52(6), 499–507 (2001)CrossRefGoogle Scholar
  13. 13.
    Flanagin, A.J., Metzger, M.J.: Perceptions of internet information credibility. Journalism Mass Commun. Q. 77, 515–540 (2000)CrossRefGoogle Scholar
  14. 14.
    Gilly, M.C., Graham, J.L., Wolfinbarger, M.F., Yale, L.J.: A dyadic study of interpersonal information search. J. Acad. Mark. Sci. 26(2), 83–100 (1998)CrossRefGoogle Scholar
  15. 15.
    Gretzel, U., Fesenmaier, D.R., O’Leary, J.T.: The transformation of consumer behaviour. In: Buhalis, D., Costa, C. (eds.) Tourism Business Frontiers, pp. 9–18. Elsevier/Butterworth-Heinemann, Burlington (2006)CrossRefGoogle Scholar
  16. 16.
    Gupta, A., Kumaraguru, P.: Credibility ranking of tweets during high impact events. In: Proceedings of the 1st Workshop on Privacy and Security in Online Social Media (PSOSM 2012) (2012)Google Scholar
  17. 17.
    Herr, P.M., Kardes, F.R., Kim, J.: Effects of word-of-mouth and product attribute information on persuasion: an accessibility-diagnosticity perspective. J. Consum. Res. 17, 454–462 (1991)CrossRefGoogle Scholar
  18. 18.
    Horrigan, J.: The Internet and consumer choice, 7 January 2015 (2008).
  19. 19.
    Hunt, J.M., Smith, M.F.: The persuasive impact of two-sided selling appeals for an unknown brand name. J. Acad. Mark. Sci. 15(1), 11–18 (1987)CrossRefGoogle Scholar
  20. 20.
    Ilgen, D.R., Fisher, C.D., Taylor, M.S.: Consequences of individual feedback on behavior in organizations. J. Appl. Psychol. 64(4), 349–371 (1979)CrossRefGoogle Scholar
  21. 21.
    Kittur, A., Chi, E.H., Suh, B.: Crowdsourcing user studies with mechanical turk. In: Proceedings of the ACM Conference on Human-factors in Computing Systems (CHI 2008), pp. 453–456 (2008)Google Scholar
  22. 22.
    Kittur, A., Suh, B., Chi, E.H.: Can you ever trust a wiki? impacting perceived trustworthiness in wikipedia. In: Proceedings of Computer-Supported Cooperative Work (CSCW 2008), pp. 477–480 (2008)Google Scholar
  23. 23.
    Kusumasondjaja, S., Shanka, T., Marchegiani, C.: Credibility of online reviews and initial trust: The roles of reviewer’s identity and review valence. J. Vacation Mark. 18(3), 185–195 (2012)CrossRefGoogle Scholar
  24. 24.
    Litvin, S.W., Goldsmith, R.E., Pan, B.: Electronic word-of-mouth in hospitality and tourism management. Tourism Manag. 29(3), 458–468 (2008)CrossRefGoogle Scholar
  25. 25.
    Mackiewicz, J.: Reviewer motivations, bias, and credibility in online reviews. In: Kelsey, S., Amant, K.S. (eds.) Handbook of Research on Computer Mediated Communication, pp. 252–266. IGI Global, Hershey (2008)CrossRefGoogle Scholar
  26. 26.
    Metzger, M.J., Flanagin, A.J., Medders, R.B.: Social and heuristic approaches to credibility evaluation online. J. Commun. 60(3), 413–439 (2010)CrossRefGoogle Scholar
  27. 27.
    Milne, G.R., Rohm, A.J., Bahl, S.: Consumers’ protection of online privacy and identity. J. Consum. Aff. 38(2), 217–232 (2004)CrossRefGoogle Scholar
  28. 28.
    Mizerski, R.W.: An attribution explanation of the disproportionate influence of unfavorable information. J. Consum. Res. 9, 301–310 (1982)CrossRefGoogle Scholar
  29. 29.
    Ott, M., Cardie, C., Hancock, J.: Estimating the prevalence of deception in online review communities. In: Proceedings of WWW 2012 (2012)Google Scholar
  30. 30.
    Pennebaker, J.W., Francis, M.E., Booth, R.J.: Linguistic inquiry and word count (LIWC) [computer software]. Erlbaum, Mahwah (2001)Google Scholar
  31. 31.
    Shay, R., Ion, I., Reeder, R.W., Consolvo, S.: My religious aunt asked why I was trying to sell her viagra: experiences with account hijacking. In: Proceedings of CHI 2014, pp. 2657−2666 (2011)Google Scholar
  32. 32.
    Sheth, J.N.: Word of mouth in low risk innovations. J. Adv. Res. 11, 15–18 (1971)Google Scholar
  33. 33.
    Strauss, A., Corbin, J.: Basics of Qualitative Research: Grounded Theory Procedures And Techniques. Sage, Newbury Park (1990)Google Scholar
  34. 34.
    Sundar, S.S.: The MAIN model: a heuristic approach to understanding technology effects on credibility. In: Metzger, M.J., Flanagin, A.J. (eds.) Digital Media Youth Credibility, pp. 73–100. The MIT Press, Cambridge (2008)Google Scholar
  35. 35.
    SPSS Technical report-linear effects mixed modeling.
  36. 36.
    Sweeney, J.C., Soutar, G.N., Mazzarol, T.: Factors influencing word of mouth effectiveness: receiver perspectives. Eur. J. Mark. 42(3/4), 344–364 (2007)CrossRefGoogle Scholar
  37. 37.
    Xie, H., Miao, L., Kuo, P.-J., Lee, B.-Y.: Consumers’ responses to ambivalent online hotel reviews: The role of perceived source credibility and pre-decisional disposition. Int. J. Hospitality Manag. 30, 178–183 (2011)CrossRefGoogle Scholar
  38. 38.
    Xu, R.: Measuring explained variation in linear mixed effects models. Stat. Med. 22(22), 3527–3541 (2003)CrossRefGoogle Scholar

Copyright information

© IFIP International Federation for Information Processing 2015

Authors and Affiliations

  • Marina Kobayashi
    • 1
  • Victoria Schwanda Sosik
    • 1
    Email author
  • David Huffaker
    • 1
  1. 1.Google, Inc.Mountain ViewUSA

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