경영정보학연구 (Information Systems Review)

  • 제23권1호
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  • Pages.45-68
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  • 2021
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  • 2982-6551(pISSN)
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  • 2982-6837(eISSN)
한국경영정보학회 (The Korea Society of Management Information Systems)
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조작된 리뷰(Fake Review)는 무엇이 다른가?

What's Different about Fake Review?

  • 이중원 (고려대학교 대학원 기업경영학과) ;
  • 박철 (고려대학교 융합경영학부)
  • Jung Won Lee (Department of Corporate Management, Korea University) ;
  • Cheol Park (College of Global Business, Korea University)
  • 투고 : 2020.08.27
  • 심사 : 2020.10.13
  • 발행 : 2021.02.28
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초록

온라인 리뷰가 소비자 의사결정에 미치는 영향이 증가함에 따라 리뷰조작에 대한 염려도 증가하고 있다. 리뷰조작은 판매량을 증가시키기 위해, 진실 되지 않은 리뷰를 게시하는 것으로 소비자의 역 선택을 초래하며, 사회 전체에 큰 비용으로 작용한다. 선행연구는 대부분 데이터 마이닝 방법을 통해 리뷰조작을 예측하는 데 초점을 맞추었으며, 소비자 관점의 연구는 상대적으로 제한적이다. 그러나 소비자가 지각한 리뷰의 조작 가능성은 리뷰의 유용성에 영향을 미칠 수 있으므로 허위 여부와 상관없이 온라인 구전 관리에 중요한 시사점을 제공할 수 있다. 따라서 본 연구에는 소비자가 조작되었다고 평가한 리뷰와 일반적인 리뷰 간에 어떠한 차이가 있는지 분석하고, 조작된 것으로 평가된 리뷰와 리뷰 유용성 간의 관계를 분석하였다. 실증분석을 위해 LibraryThing 웹사이트의 온라인 도서 리뷰 34,711개를 다수준 로지스틱 회귀분석과 포아송 회귀분석을 활용하여 분석하였다. 분석결과 소비자가 조작되었다고 지각하는 리뷰와 그렇지 않은 리뷰 간에는 제품 수준, 리뷰어 수준, 리뷰 수준 요인들에 차이가 있는 것으로 나타났다. 또한, 조작된 리뷰는 리뷰 유용성에 부정적인 영향을 미치는 것으로 나타났다.

As the influence of online reviews on consumer decision-making increases, concerns about review manipulation are also increasing. Fake reviews or review manipulations are emerging as an important problem by posting untrue reviews in order to increase sales volume, causing the consumer's reverse choice, and acting at a high cost to the society as a whole. Most of the related prior studies have focused on predicting review manipulation through data mining methods, and research from a consumer perspective is insufficient. However, since the possibility of manipulation of reviews perceived by consumers can affect the usefulness of reviews, it can provide important implications for online word-of-mouth management regardless of whether it is false or not. Therefore, in this study, we analyzed whether there is a difference between the review evaluated by the consumer as being manipulated and the general review, and verified whether the manipulated review negatively affects the review usefulness. For empirical analysis, 34,711 online book reviews on the LibraryThing website were analyzed using multilevel logistic regression analysis and Poisson regression analysis. As a result of the analysis, it was found that there were differences in product level, reviewer level, and review level factors between reviews that consumers perceived as being manipulated and reviews that were not. In addition, manipulated reviews have been shown to negatively affect review usefulness.

키워드

참고문헌

  1. 김민재, 이상진, "코사인 유사도 기반의 인터넷 댓글 상 이상 행위 분석 방법", 정보보호학회논문지, 제24권, 제2호, 2014, pp. 335-343.  https://doi.org/10.13089/JKIISC.2014.24.2.335
  2. 노영주, "빅데이터 분석을 활용한 가짜 리뷰 필터링 시스템 ADDAVICHI", 한국인터넷방송통신학회 논문지, 제19권, 제6호, 2019, pp. 1-8. 
  3. 오영교, 구동영, "LSTM(Long Short-Term Memory) 을 이용한 가짜 리뷰 생성과 분석 및 평가", 정보과학회논문지, 제46권, 제6호, 2019, pp. 515-525. 
  4. 윤진숙, 박철, "인터넷쇼핑몰의 소비자기만 행동에 관한 내용분석", 인터넷전자상거래연구, 제14권, 제3호, 2014, pp. 15-35. 
  5. 이윤혜, 박철, "육아커뮤니티에서 온라인 구전정보 조회 수에 영향을 미치는 요인에 관한 연구", 인터넷전자상거래연구, 제16권, 제3호, 2016, pp. 215-238. 
  6. 이중원, 박철, "온라인 구전의 방향성과 분산이 영화매출에 미치는 영향 경쟁영화 온라인 구전 특성의 조절효과를 중심으로", 경영학연구, 제48권, 제2호, 2019b, pp. 341-360. 
  7. 이중원, 박철, "온라인 구전이 영화매출에 미치는 영향: 소유미디어와 획득미디어의 조절 효과를 중심으로", Information Systems Review, 제21권, 제2호, 2019a, pp. 29-50.  https://doi.org/10.14329/isr.2019.21.2.029
  8. 이태민, 박철, "The effects of eWOM objectivity on perceived credibility: Investigating the moderating role of national culture and product type", 상품학연구, 제27권, 3호, 2009, pp. 49-60.  https://doi.org/10.36345/KACST.2009.27.3.004
  9. 이현애, 정남호, 구철모(2017), "호텔 등급에 따른 온라인 리뷰 유형과 유용성의 관계 분석", 경영학연구, 제46권, 제1호, pp. 137-156. 
  10. 정옥경, 박철, "의료서비스상품의 온라인 구전 조회수에 영향을 미치는 구전정보특성에 관한 분석", 상품학연구, 제36권, 제3호, 2018, pp. 41-48.  https://doi.org/10.36345/KACST.2018.36.3.005
  11. Abbasi, A. and H. Chen, "CyberGate: A design framework and system for text analysis of computer-mediated communication", Mis Quarterly, Vol.32, No.4, 2008, pp. 811-837.  https://doi.org/10.2307/25148873
  12. Ahmad, S. N. and M. Laroche, "How do expressed emotions affect the helpfulness of a product review? Evidence from reviews using latent semantic analysis", International Journal of Electronic Commerce, Vol.20, No.1, 2005, pp. 76-111.  https://doi.org/10.1080/10864415.2016.1061471
  13. Akpinar, E., P. W. Verlegh, and A. Smidts, "Sharing product harm information: The effects of self-construal and self-relevance", International Journal of Research in Marketing, Vol.35, No.2, 2018, pp. 319-335.  https://doi.org/10.1016/j.ijresmar.2018.01.001
  14. Aleti, T., J. I. Pallant, A. Tuan, and T. van Laer, "Tweeting with the stars: Automated text analysis of the effect of celebrity social media communications on consumer word of mouth", Journal of Interactive Marketing, Vol.48, 2019, pp. 17-32.  https://doi.org/10.1016/j.intmar.2019.03.003
  15. Babic Rosario, A., F. Sotgiu, K. De Valck, and T. H. Bijmolt, "The effect of electronic word of mouth on sales: A meta-analytic review of platform, product, and metric factors", Journal of Marketing Research, Vol.53, No.3, 2016, pp. 297-318.  https://doi.org/10.1509/jmr.14.0380
  16. Barasch, A. and J. Berger, "Broadcasting and narrowcasting: How audience size affects what people share", Journal of Marketing Research, Vol.51, No.3, 2014, pp. 286-299.  https://doi.org/10.1509/jmr.13.0238
  17. Berger, J., A. T. Sorensen, and S. J. Rasmussen, "Positive effects of negative publicity: When negative reviews increase sales", Marketing Science, Vol.29, No.5, 2010, pp. 815-827.  https://doi.org/10.1287/mksc.1090.0557
  18. Cheng, Y. H. and H. Y. Ho, "Social influence's impact on reader perceptions of online reviews", Journal of Business Research, Vol.68, No.4, 2015, pp. 883-887.  https://doi.org/10.1016/j.jbusres.2014.11.046
  19. Chevalier, J. A. and D. Mayzlin, "The effect of word of mouth on sales: Online book reviews", Journal of Marketing Research, Vol.43, No.3, 2006, pp. 345-354.  https://doi.org/10.1509/jmkr.43.3.345
  20. Darke, P. R. and R. J. Ritchie, "The defensive consumer: Advertising deception, defensive processing, and distrust", Journal of Marketing Research, Vol.44, No.1, 2007, pp. 114-127.  https://doi.org/10.1509/jmkr.44.1.114
  21. Dehejia, R. H. and S. Wahba, "Propensity score-matching methods for nonexperimental causal studies", Review of Economics and Statistics, Vol.84, No.1, 2002, pp. 151-161.  https://doi.org/10.1162/003465302317331982
  22. Dellarocas, C., "Strategic manipulation of internet opinion forums: Implications for consumers and firms", Management Science, Vol.52, No.10, 2006, pp. 1577-1593.  https://doi.org/10.1287/mnsc.1060.0567
  23. DePaulo, B. M., J. J. Lindsay, B. E. Malone, L. Muhlenbruck, K. Charlton, and H. Cooper, "Cues to deception", Psychological Bulletin, Vol.129, No.1, 2003, pp. 74-118.  https://doi.org/10.1037/0033-2909.129.1.74
  24. Forman, C., A. Ghose, and B. Wiesenfeld, "Examining the relationship between reviews and sales: The role of reviewer identity disclosure in electronic markets", Information Systems Research, Vol.19, No.3, 2008, pp. 291-313.  https://doi.org/10.1287/isre.1080.0193
  25. Fuller, C. M., D. P. Biros, and R. L.Wilson, "Decision support for determining veracity via linguistic-based cues", Decision Support Systems, Vol.46, No.3, 2009, pp. 695-703.  https://doi.org/10.1016/j.dss.2008.11.001
  26. George, J. F., K. Marett, and G. Giordano, "Deception: Toward an individualistic view of group support systems", Journal of the Association for Information Systems, Vol.9, No.10, 2008, pp. 656-676. 
  27. Guo, B. and S. Zhou, "What makes population perception of review helpfulness: An information processing perspective", Electronic Commerce Research, Vol.17, No.4, 2017, pp. 585-608.  https://doi.org/10.1007/s10660-016-9234-7
  28. Hair, M. and T. Ozcan, "How reviewers' use of profanity affects perceived usefulness of online reviews", Marketing Letters, Vol.29, No.2, 2018, pp. 151-163.  https://doi.org/10.1007/s11002-018-9459-4
  29. Hancock, J. T., L. E. Curry, S. Goorha, and M. Woodworth, "On lying and being lied to: A linguistic analysis of deception in computer-mediated communication", Discourse Processes, Vol.45, No.1, 2007, pp. 1-23.  https://doi.org/10.1080/01638530701739181
  30. Hong, H., D. Xu, G. A. Wang, and W. Fan, "Understanding the determinants of online review helpfulness: A meta-analytic investigation", Decision Support Systems, Vol.102, 2017, pp. 1-11.  https://doi.org/10.1016/j.dss.2017.06.007
  31. Hu, N., I. Bose, N. S. Koh, and L. Liu, "Manipulation of online reviews: An analysis of ratings, readability, and sentiments", Decision Support Systems, Vol.52, No.3, 2012, pp. 674-684.  https://doi.org/10.1016/j.dss.2011.11.002
  32. Hu, N., L. Liu, and V. Sambamurthy, "Fraud detection in online consumer reviews", Decision Support Systems, Vol.50, No.3, 2011, pp. 614-626.  https://doi.org/10.1016/j.dss.2010.08.012
  33. Huang, A. H., K. Chen, D. C. Yen, and T. P. Tran, "A study of factors that contribute to online review helpfulness", Computers in Human Behavior, Vol.48, 2015, pp. 17-27.  https://doi.org/10.1016/j.chb.2015.01.010
  34. Humpherys, S. L., K. C. Moffitt, M. B. Burns, J. K. Burgoon, and W. F. Felix, "Identification of fraudulent financial statements using linguistic credibility analysis", Decision Support Systems, Vol.50, No.3, 2011, pp. 585-594.  https://doi.org/10.1016/j.dss.2010.08.009
  35. Humphreys, A. and R. J. H. Wang, "Automated text analysis for consumer research", Journal of Consumer Research, Vol.44, No.6, 2018, pp. 1274-1306.  https://doi.org/10.1093/jcr/ucx104
  36. Jensen, M. L., J. M. Averbeck, Z. Zhang, and K. B. Wright, "Credibility of anonymous online product reviews: A language expectancy perspective", Journal of Management Information Systems, Vol.30, No.1, 2013, pp. 293-324. 
  37. Jindal, N. and B. Liu, "Opinion spam and analysis", In Proceedings of the 2008 International Conference on Web Search and Data Mining, 2008, pp. 219-230. 
  38. Johnen, M. and O. Schnittka, "When pushing back is good: The effectiveness of brand responses to social media complaints", Journal of the Academy of Marketing Science, Vol.47, No.5, 2019, pp. 858-878.  https://doi.org/10.1007/s11747-019-00661-x
  39. Karimi, S. and F. Wang, "Online review helpfulness: Impact of reviewer profile image", Decision Support Systems, Vol.96, 2017, pp. 39-48.  https://doi.org/10.1016/j.dss.2017.02.001
  40. Kim, Y. J. and A. B. Hollingshead, "Online social influence: Past, present, and future", Annals of the International Communication Association, Vol.39, No.1, 2015, pp. 163-192.  https://doi.org/10.1080/23808985.2015.11679175
  41. Kumar, N., D. Venugopal, L. Qiu, and S. Kumar, "Detecting review manipulation on online platforms with hierarchical supervised learning", Journal of Management Information Systems, Vol.35, No.1, 2018, pp. 350-380.  https://doi.org/10.1080/07421222.2018.1440758
  42. Lagace, R. R., R. Dahlstrom, and J. B. Gassenheimer, "The relevance of ethical salesperson behavior on relationship quality: The pharmaceutical industry", Journal of Personal Selling & Sales Management, Vol.11, No.4, 1991, pp. 39-47. 
  43. Li, K., Y. Chen, and L. Zhang, "Exploring the influence of online reviews and motivating factors on sales: A meta-analytic study and the moderating role of product category", Journal of Retailing and Consumer Services, Vol.55, No.102107, 2020, pp. 1-11. 
  44. Liu, Z. and S. Park, "What makes a useful online review? Implication for travel product websites", Tourism Management, Vol.47, 2015, pp. 140-151.  https://doi.org/10.1016/j.tourman.2014.09.020
  45. Lovett, M. J., R. Peres, and R. Shachar, "On brands and word of mouth", Journal of Marketing Research, Vol.50, No.4, 2013, pp. 427-444.  https://doi.org/10.1509/jmr.11.0458
  46. Ludwig, S., T. Van Laer, K. De Ruyter, and M. Friedman, "Untangling a web of lies: Exploring automated detection of deception in computer-mediated communication", Journal of Management Information Systems, Vol.33, No.2, 2016, pp. 511-541.  https://doi.org/10.1080/07421222.2016.1205927
  47. Masip, J., E. Garrido, and C. Herrero, "Facial appearance and impressions of 'credibility': The effects of facial babyishness and age on person perception", International Journal of Psychology, Vol.39, No.4, pp. 276-289. 
  48. Matsumoto, D., H. C. Hwang, and V. A. Sandoval, "Cross-language applicability of linguistic features associated with veracity and deception", Journal of Police and Criminal Psychology, Vol.30, No.4, 2015, pp. 229-241.  https://doi.org/10.1007/s11896-014-9155-0
  49. Mayzlin, D., "Promotional chat on the Internet", Marketing Science, Vol.25, No.2, 2006, pp. 155-163.  https://doi.org/10.1287/mksc.1050.0137
  50. Mayzlin, D., Y. Dover, and J. Chevalier, "Promotional reviews: An empirical investigation of online review manipulation", American Economic Review, Vol.104, No.8, 2014, pp. 2421-2455.  https://doi.org/10.1257/aer.104.8.2421
  51. Moon, S., M. Y. Kim, and P. K. Bergey, "Estimating deception in consumer reviews based on extreme terms: Comparison analysis of open vs. closed hotel reservation platforms", Journal of Business Research, Vol.102, 2019, pp. 83-96.  https://doi.org/10.1016/j.jbusres.2019.05.016
  52. Ong, T., M. Mannino, and D. Gregg, "Linguistic characteristics of shill reviews", Electronic Commerce Research and Applications, Vol.13, No.2, 2014, pp. 69-78.  https://doi.org/10.1016/j.elerap.2013.10.002
  53. Park, C. and T. M. Lee, "Information direction, website reputation and eWOM effect: A moderating role of product type", Journal of Business Research, Vol.62, No.1, 2009, pp. 61-67.  https://doi.org/10.1016/j.jbusres.2007.11.017
  54. Park, S. and J. L. Nicolau, "Asymmetric effects of online consumer reviews", Annals of Tourism Research, Vol.50, 2015, pp. 67-83.  https://doi.org/10.1016/j.annals.2014.10.007
  55. Peng, L., G. Cui, M. Zhuang, and C. Li, "Consumer perceptions of online review deceptions: An empirical study in China", Journal of Consumer Marketing, Vol.33, No.4, 2016, pp. 269-280.  https://doi.org/10.1108/JCM-01-2015-1281
  56. Pennebaker, J. W., C. K. Chung, J. Frazee, G. M. Lavergne, and D. I. Beaver, "When small words foretell academic success: The case of college admissions essays", PloS One, Vol.9, No.12, 2014, e115844. 
  57. Pennebaker, J. W., R. L. Boyd, K. Jordan, and K. Blackburn, The development and psychometric properties of LIWC2015, Pennebaker Conglomerates Inc, Austin, 2015. 
  58. Purnawirawan, N., P. De Pelsmacker, and N. Dens, "Balance and sequence in online reviews: How perceived usefulness affects attitudes and intentions", Journal of Interactive Marketing, Vol.26, No.4, 2012, pp. 244-255.  https://doi.org/10.1016/j.intmar.2012.04.002
  59. Roman, S. and S. Ruiz, "Relationship outcomes of perceived ethical sales behavior: The customer's perspective", Journal of Business Research, Vol.58, No.4, 2005, pp. 439-445.  https://doi.org/10.1016/j.jbusres.2003.07.002
  60. Seo, Y., X. Li, Y. K. Choi, and S. Yoon, "Narrative transportation and paratextual features of social media in viral advertising", Journal of Advertising, Vol.47, No.1, 2018, pp. 83-95.  https://doi.org/10.1080/00913367.2017.1405752
  61. Sundaram, D. S., K. Mitra, and C. Webster, "Word-of-mouth communications: A motivational analysis", ACR North American Advances, Vol.25, 1998, pp. 527-531. 
  62. Tausczik, Y. R. and J. W. Pennebaker, "The psychological meaning of words: LIWC and computerized text analysis methods", Journal of Language and Social Psychology, Vol.29, No.1, 2010, pp. 24-54.  https://doi.org/10.1177/0261927X09351676
  63. Tirunillai, S. and G. J. Tellis, "Does chatter really matter? Dynamics of user-generated content and stock performance", Marketing Science, Vol.31, No.2, 2012, pp. 198-215.  https://doi.org/10.1287/mksc.1110.0682
  64. Van Laer, T., J. Edson Escalas, S. Ludwig, and E. A. Van Den Hende, "What happens in Vegas stays on TripAdvisor? A theory and technique to understand narrativity in consumer reviews", Journal of Consumer Research, Vol.46, No.2, 2019, pp. 267-285. 
  65. Xiao, B. and I. Benbasat, "The asymmetric effects of trust and distrust: An empirical investigation in a deception detection context", Proceedings of SIGHCI, 2010. 
  66. Zhao, T., J. McAuley, and I. King, "Improving latent factor models via personalized feature projection for one class recommendation", In Proceedings of the 24th ACM international on conference on information and knowledge management, 2015, pp. 821-830. 
  67. Zhou, L. and A. Zenebe, "Representation and reasoning under uncertainty in deception detection: A neuro-fuzzy approach", IEEE Transactions on Fuzzy Systems, Vol.16, No.2, 2008, pp. 442-454.  https://doi.org/10.1109/TFUZZ.2006.889914
  68. Zhou, L., J. K. Burgoon, J. F. Nunamaker, and D. Twitchell, "Automating linguistics-based cues for detecting deception in text-based asynchronous computer-mediated communications", Group Decision and Negotiation, Vol.13, No.1, 2004, pp. 81-106.  https://doi.org/10.1023/B:GRUP.0000011944.62889.6f
  69. Zuckerman, M., B. M. DePaulo, and R. Rosenthal, "Verbal and nonverbal communication of deceptionm", In Advances in Experimental Social Psychology, Vol.14, 1981, pp. 1-59. https://doi.org/10.1016/S0065-2601(08)60369-X