3,674
Views
12
CrossRef citations to date
0
Altmetric

References

  • Abbasi, A.; Zahedi., F.M.; Zeng, D.; Chen, Y.; Chen, H.; and Nunamaker, J.F. Jr. Enhancing predictive analytics for anti-phishing by exploiting website genre information. Journal of Management Information Systems, 31, 4 (2015), 109–157. doi:10.1080/07421222.2014.1001260
  • Adomavicius, G.; Bockstedt, J.C.; Curley, S.P.; and Zhang, J. Do recommender systems manipulate consumer preferences? A study of anchoring effects. Information Systems Research, 24, 4 (2013), 956–975. doi:10.1287/isre.2013.0497
  • Aggarwal, R., and Singh, H. Differential influence of blogs across different stages of decision making: The case of venture capitalists. MIS Quarterly, 37, 4 (2013), 1093–1112. doi:10.25300/MISQ
  • Akoglu, L.; Chandy, R.; and Faloutsos, C. Opinion fraud detection in online reviews by network effects. Proceedings of the International AAAI Conference on Weblogs and Social Media, 7, (2013), 2–11.
  • Anderson, M., and Magruder, J. Learning from the crowd: Regression discontinuity estimates of the effects of an online review database. Economic Journal, 122, 563 (2012), 957–989. doi:10.1111/ecoj.2012.122.issue-563
  • Associated Press. Fake online reviews: Here are some tips for detecting them. NBC News. 2015. http://www.nbcnews.com/business/consumer/fake-online-reviews-here-are-some-tips-detecting-them-n447681 (accessed on August 10, 2017).
  • Banerjee, S., and Chua, A.Y.K. A study of manipulative and authentic negative reviews. Proceedings of the International Conference on Ubiquitous Information Management and Communication, 8, (2014), 1–6.
  • Benjamin, V.; Zhang, B.; Nunamaker, J.F. Jr; and Chen, H. Examining hacker participation length in cybercriminal Internet-relay-chat communities. Journal of Management Information Systems, 33, 2 (2016), 482–510. doi:10.1080/07421222.2016.1205918
  • Box, G.E., and Cox, D.R. An analysis of transformations. Journal of the Royal Statistical Society. Series B (Methodological), 26, 2 (1964), 211–252.
  • Branco, F., and Villas-Boas, J.M. Competitive vices. Journal of Marketing Research, 52, 6 (2015), 801–816. doi:10.1509/jmr.13.0051
  • Breiman, L. Random forests. Machine Learning, 45, 1 (2001), 5–32. doi:10.1023/A:1010933404324
  • Breiman, L.; Friedman, J.; Stone, C.J.; and Olshen, R.A. Classification and Regression Trees. Boca Raton: CRC Press, 1984.
  • Cortes, C., and Vapnik, V. Support-vector networks. Machine Learning, 20, 3 (1995), 273–297. doi:10.1007/BF00994018
  • Dalvi, N.N.; Kumar, R.; and Pang, B. Para “normal” activity: On the distribution of average ratings. Proceedings of International Conference on Weblogs and Social Media, 7, (2013), 110–119.
  • Dave, K.; Lawrence, S.; and Pennock, D.M. Mining the peanut gallery: Opinion extraction and semantic classification of product reviews. Proceedings of the International Conference on World Wide Web, 12 (2003), 519–528.
  • Domingos, P., and Pazzani, M. On the optimality of the simple Bayesian classifier under zero-one loss. Machine Learning, 29, 2 (1997), 103–130. doi:10.1023/A:1007413511361
  • Duan, H., and Zirn, C. Can we identify manipulative behavior and the corresponding suspects on review websites using supervised learning? Lecture Notes in Computer Science, 7617 (2012), 215–230.
  • Fei, G.; Mukherjee, A.; Liu, B.; Hsu, M.; Castellanos, M.; and Ghosh, R. Exploiting burstiness in reviews for review spammer detection. Proceedings of the International AAAI Conference on Weblogs and Social Media, 7 (2013), 175–184.
  • Feng, S.; Banerjee, R.; and Choi, Y. Syntactic stylometry for deception detection. Proceedings of the Annual Meeting of the Association for Computational Linguistics, 50 (2012), 171–175.
  • Forman, C.; Ghose, A.; and Wiesenfeld, B. Examining the relationship between reviews and sales: the role of reviewer identity disclosure in electronic markets. Information Systems Research, 19, 3 (2008), 291–313. doi:10.1287/isre.1080.0193
  • Freeman, L.L. How to spot fake online reviews. Time, July 22, 2016. http://time.com/money/4362586/fake-online-reviews/ (accessed on August 10, 2017)
  • Freund, Y., and Schapire, R.E. A short introduction to boosting. Journal of Japanese Society for Artificial Intelligence, 14, 5 (1999), 771–780.
  • Fuscaldo, D. How to spot fake reviews online. Fox News. June 27, 2014. http://www.foxbusiness.com/features/2014/06/27/how-to-spot-fake-online-reviews.html (accessed onAugust 10, 2017).
  • Gallivan, R. Amid fake reviews, consumers are skeptical of social media marketing. Wall Street Journal, June 3, 2014. http://blogs.wsj.com/digits/2014/06/03/amid-fake-reviews-consumers-skeptical-of-social-media-marketing/ (accessed on August 10, 2017).
  • Goes, P.B.; Lin, M.; and Au Yeung, C.M. Popularity effect” in user-generated content: Evidence from online product reviews. Information Systems Research, 25, 2 (2014), 222–238. doi:10.1287/isre.2013.0512
  • Goswami, K.; Park, Y.; and Song, C. Impact of reviewer social interaction on online consumer review fraud detection. Journal of Big Data, 4, 1 (2017), 1–19. doi:10.1186/s40537-017-0075-6
  • Gu, B., and Ye, Q. First step in social media: measuring the influence of online management responses on customer satisfaction. Production and Operations Management, 23, 4 (2014), 570–582. doi:10.1111/poms.2014.23.issue-4
  • Ho, S.M.; Hancock, J.T.; Booth, C.; and Liu, X. Computer-mediated deception: Strategies revealed by language-action cues in spontaneous communication. Journal of Management Information Systems, 33, 2 (2016), 393–420. doi:10.1080/07421222.2016.1205924
  • Hu, N.; Bose, I.; Gao, Y.; and Liu, L. Manipulation in digital word-of-mouth: A reality check for book reviews. Decision Support Systems, 50, 3 (2011), 627–635. doi:10.1016/j.dss.2010.08.013
  • Hu, N.; Liu, L.; and Sambamurthy, V. Fraud detection in online consumer reviews. Decision Support Systems, 50, 2 (2011), 614–626. doi:10.1016/j.dss.2010.08.012
  • Hu, N.; Zhang, J.; and Pavlou, P.A. Overcoming the J-shaped distribution of product reviews. Communications of the ACM, 52, 10 (2009), 144–147. doi:10.1145/1562764
  • Jindal, N., and Liu, B. Opinion spam and analysis. In Proceedings of the International Conference on Web Search and Data Mining. New York,. NY: ACM, 2008, pp. 219–230.
  • Khansa, L.; Ma, X.; Liginlal, D.; and Kim, S.S. Understanding members’ active participation in online question-and-answer communities: A theory and empirical analysis. Journal of Management Information Systems, 32, 2 (2015), 162–203. doi:10.1080/07421222.2015.1063293
  • Kumar, N.; Qiu, L.; and Kumar, S. Exit, voice, and response on digital platforms: An empirical investigation of online management response strategies. Information Systems Research (2018), Forthcoming.
  • Lahiri, A.; Dewan, R.M.; and Freimer, M. The disruptive effect of open platforms on markets for wireless services. Journal of Management Information Systems, 27, 3 (2010), 81–110. doi:10.2753/MIS0742-1222270304
  • Lappas, T.; Sabnis, G.; and Valkanas, G. The impact of fake reviews on online visibility: A vulnerability assessment of the hotel industry. Information Systems Research, 27, 4 (2016), 940–961. doi:10.1287/isre.2016.0674
  • Lau, R.Y.K.; Liao, S.Y.; Kwok, R.C.W.; Xu, K.; Xia, Y.; and Li, Y. Text mining and probabilistic language modeling for online review spam detection. Transactions on Management Information Systems, ACM, 2, 4 (2011), 1–30. doi:10.1145/2070710.2070716
  • Lee, S.; Qiu, L.; and Whinston, A.B. Sentiment manipulation in online platforms and opinion forums: An analysis of movie tweets. Working paper, University of Texas Austin, 2016.
  • Li, F.; Huang, M.; Yang, Y.; and Zhu, X. Learning to identify review spam. Proceedings of the International Joint Conference on Artificial Intelligence, 22, 3 (2011), 2488–2493.
  • Li, J.; Ott, M.; Cardie, C.; and Hovy, E.H. Towards a general rule for identifying deceptive opinion spam. Proceedings of the Annual Meeting of the Association for Computational Linguistics, 52, (2014), 1566–1576.
  • Li, X. Could deal promotion improve merchants’ online reputations? The moderating role of prior reviews. Journal of Management Information Systems, 33, 1 (2016), 171–201. doi:10.1080/07421222.2016.1172450
  • Liang, N.; Biros, D.P.; and Luse, A. An empirical validation of malicious insider characteristics. Journal of Management Information Systems, 33, 2 (2016), 361–392. doi:10.1080/07421222.2016.1205925
  • Lim, E.P.; Nguyen, V.A.; Jindal, N.; Liu, B.; and Lauw, H.W. Detecting product review spammers using rating behaviors. Proceedings of the ACM International Conference on Information and Knowledge Management, 19, (2010), 939–948.
  • Lin, Y.; Zhu, T.; Wu, H.; Zhang, J.; Wang, X.; and Zhou, A. Towards online anti-opinion spam: spotting fake reviews from the review sequence. In Proceedings of International Conference on Advances in Social Networks Analysis and Mining. IEEE Computer Society, 2014, pp. 261–264.
  • Luca, M. Reviews, reputation, and revenue: The case of Yelp.com. 2011. doi:10.2139/ssrn.1928601
  • Luca, M., and Zervas, G. Fake it till you make it: Reputation, competition, and Yelp review fraud. Management Science, 62, 12 (2016), 3412–3427. doi:10.1287/mnsc.2015.2304
  • Ludwig, S.; Van Laer, T.; De Ruyter, K.; and Friedman, M. Untangling a web of lies: Exploring automated detection of deception in computer-mediated communication. Journal of Management Information Systems, 33, 2 (2016), 511–541. doi:10.1080/07421222.2016.1205927
  • Luo, X., and Zhang, J. How do consumer buzz and traffic in social media marketing predict the value of the firm? Journal of Management Information Systems, 30, 2 (2013), 213–238.
  • Ma, M., and Agarwal, R. Through a glass darkly: information technology design, identity verification, and knowledge contribution in online communities. Information Systems Research, 18, 1 (2007), 42–67. doi:10.1287/isre.1070.0113
  • Mantena, R., and Saha, R.L. Co-opetition between differentiated platforms in two-sided markets. Journal of Management Information Systems, 29, 2 (2012), 109–140. doi:10.2753/MIS0742-1222290205
  • Mayzlin, D. Promotional chat on the Internet. Marketing Science, 25, 2 (2006), 155–163. doi:10.1287/mksc.1050.0137
  • Mayzlin, D.; Dover, Y.; and Chevalier, J. Promotional reviews: An empirical investigation of online review manipulation. American Economic Review, 104, 8 (2014), 2421–2455. doi:10.1257/aer.104.8.2421
  • Minka, T. Estimating a Dirichlet distribution. Technical report, MIT, 2000.
  • Mintel. Seven in 10 Americans seek out opinions before making purchases. 2015. http://www.mintel.com/press-centre/social-and-lifestyle/seven-in-10-americans-seek-out-opinions-before-making-purchases (accessed on August 10, 2017).
  • Mukherjee, A.; Kumar, A.; Liu, B.; Wang, J.; Hsu, M.; Castellanos, M.; and Ghosh, R. Spotting opinion spammers using behavioral footprints. Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 19 (2013), 632–640.
  • Mukherjee, A.; Liu, B.; and Glance, N. Spotting fake reviewer groups in consumer reviews. Proceedings of the International Conference on World Wide Web, 21, (2012), 191–200.
  • Mukherjee, A.; Venkataraman, V.; Liu, B.; and Glance, N.S. What Yelp fake review filter might be doing? Proceedings of the International AAAI Conference on Weblogs and Social Media, 7 (2013), 409–418.
  • Nunamaker, J.F.; Burgoon, J.K.; and Giboney, J.S. Special issue: Information systems for deception detection. Journal of Management Information Systems, 33, 2 (2016), 327–331. doi:10.1080/07421222.2016.1205928
  • Ott, M.; Cardie, C.; and Hancock, J. Estimating the prevalence of deception in online review communities. Proceedings of the International Conference on World Wide Web, 21 (2012), 201–210.
  • Ott, M.; Choi, Y.; Cardie, C.; and Hancock, J.T. Finding deceptive opinion spam by any stretch of the imagination. Proceedings of the Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, 49, 1 (2011), 309–319.
  • Proudfoot, J.G.; Jenkins, J.L.; Burgoon, J.K.; and Nunamaker, J.F. Jr. More than meets the eye: How oculometric behaviors evolve over the course of automated deception detection interactions. Journal of Management Information Systems, 33, 2 (2016), 332–360. doi:10.1080/07421222.2016.1205929
  • Rahman, M.; Carbunar, B.; Ballesteros, J.; and Chau, D.H.P. To catch a fake: Curbing deceptive yelp ratings and venues. Statistical Analysis and Data Mining: The ASA Data Science Journal, 8, 3 (2015), 147–161. doi:10.1002/sam.11264
  • Rayana, S., and Akoglu, L. Collective opinion spam detection: bridging review networks and metadata. Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 21 (2015), 985–994.
  • Roberts, J.J. Amazon sues people who charge $5 for fake reviews. Fortune Magazine. October 19, 2015. http://fortune.com/2015/10/19/amazon-fake-reviews/ (accessed on August 10, 2017).
  • Rudolph, S. The impact of online reviews on customers’ buying decisions. Business 2 Community. 2015. http://www.business2community.com/infographics/impact-online-reviews-customers-buying-decisions-infographic-01280945#iZwM69pSgVKLlH6A.97 ( accessed on August 10, 2017).
  • Siering, M.; Koch, J.A.; and Deokar, A.V. Detecting fraudulent behavior on crowd platforms: The role of linguistic and content-based cues in static and dynamic contexts. Journal of Management Information Systems, 33, 2 (2016), 421–455. doi:10.1080/07421222.2016.1205930
  • Sinha, A.P., and May, J.H. Evaluating and tuning predictive data mining models using receiver operating characteristic curves. Journal of Management Information Systems, 21, 3 (2004), 249–280. doi:10.1080/07421222.2004.11045815
  • Stoppelman, J. Why Yelp has a review filter. 2009. http://officialblog.yelp.com/2009/10/why-yelp-has-a-review-filter.html (accessed on August 10, 2017).
  • Stritfeld, D. The best book reviews money can buy. New York Times. August 26, 2012. http://www.nytimes.com/2012/08/26/business/book-reviewers-for-hire-meet-a-demand-for-online-raves.html (accessed on August 10, 2017)
  • Ting, K.M., and Witten, I.H. Stacked generalization: When does it work? Proceedings of the International Joint Conference on Artificial Intelligence, 15, 2 (1997), 866–871.
  • Tsikerdekis, M., and Zeadally, S. Online deception in social media. Communications of the ACM, 57, 9 (2014), 72–80. doi:10.1145/2663191
  • Tuttle, B. Amazon lawsuit shows that fake online reviews are a big problem. Time October 19, 2015. http://time.com/money/4078632/amazon-fake-online-reviews/ (accessed on August 10, 2017).
  • Twyman, N.W.; Proudfoot, J.G.; Schuetzler, R.M.; Elkins, A.C.; and Derrick, D.C. Robustness of multiple indicators in automated screening systems for deception detection. Journal of Management Information Systems, 32, 4 (2015), 215–245.
  • Wang, G.; Xie, S.; Liu, B.; and Philip, S.Y. Review graph based online store review spammer detection. Proceedings of the International Conference on Data Mining, 11 (2011), 1242–1247.
  • Wang, G.; Xie, S.; Liu, B.; and Yu, P.S. Identify online store review spammers via social review graph. ACM Transactions on Intelligent Systems and Technology (TIST), 3, 4 (2012), 61–82.
  • Wang, Y.; Chan, S.C.F.; Ngai, G.; and Leong, H.V. Quantifying reviewer credibility in online tourism. In Proceedings of the International Conference on Database and Expert Systems Applications New York, NY: Springer-Verlag New York, Inc., 2013, pp. 381–395.
  • Wang, Z. Anonymity, social image, and the competition for volunteers: A case study of the online market for reviews. BE Journal of Economic Analysis and Policy.Heidelberg: Springer, 10, 1 (2010), 1–35.
  • Wasko, M., and Faraj, S. Why should I share? Examining social capital and knowledge contribution in electronic networks of practice. MIS Quarterly, 29, 1 (2005), 35–57. doi:10.2307/25148667
  • Wolpert, D.H. Stacked generalization. Neural Networks, 5, 2 (1992), 241–259. doi:10.1016/S0893-6080(05)80023-1
  • Wu, G.; Greene, D.; Smyth, B.; and Cunningham, P. Distortion as a validation criterion in the identification of suspicious reviews. Proceedings of the Workshop on Social Media Analytics, 1 (2010), 10–13.
  • Ye, J., and Akoglu, L. Discovering opinion spammer groups by network footprints. In Proceedings of the Joint European Conference on Machine Learning and Knowledge Discovery in Databases. Berlin, Heidelberg: Springer, 2015, pp. 267–282.
  • Yelp. 10 things you should know about Yelp. 2016. http://www.yelp.com/about (accessed on August 10, 2017).
  • Zhang, D.; Zhou, L.; Kehoe, J.L.; and Kilic, I.Y. What online reviewer behaviors really matter? Effects of verbal and nonverbal behaviors on detection of fake online reviews. Journal of Management Information Systems, 33, 2 (2016), 456–481. doi:10.1080/07421222.2016.1205907
  • Zhang, L.; Ma, B.; and Cartwright, D.K. The impact of online user reviews on cameras sales. European Journal of Marketing, 47, 7 (2013), 1115–1128. doi:10.1108/03090561311324237
  • Zhou, R. Muddy waters. December 13, 2012. http://usa.chinadaily.com.cn/life/2012-12/13/content_16013662.htm (accessed on August 10, 2017).
  • Zhou, W., and Duan, W. Do professional reviews affect online user choices through user reviews? An empirical study. Journal of Management Information Systems, 33, 1 (2016), 202–228. doi:10.1080/07421222.2016.1172460

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.