Detecting Anomalous Online Reviewers: An Unsupervised Approach Using Mixture Models

References

• 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.
   Google Scholar
• Amer, M.; Goldstein, M.; and Abdennadher, S. Enhancing one-class support vector machines for unsupervised anomaly detection. In Proceedings of the ACM SIGKDD Workshop on Outlier Detection and Description. New York, NY: ACM, 2013, pp. 8–15.
   Google Scholar
• 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), 76:1-76:6.
   Google Scholar
• Benjamin, V.; Zhang, B.; Nunamaker Jr, J.F.; and Chen, H. Examining hacker participation length in cybercriminal Internet-relay-chat communities. Journal of Management Information Systems, 33, 2 (2016), 482–510.
   Web of Science ®Google Scholar
• Bhattarai, A.; Rus, V.; and Dasgupta, D. Characterizing comment spam in the blogosphere through content analysis. Proceedings of IEEE Symposium on Computational Intelligence in Cyber Security, 1, (2009), 37–44.
   Google Scholar
• Blanding, M. The yelp factor: Are consumer reviews good for business? Harvard Business School, 2011. https://hbswk.hbs.edu/item/the-yelp-factor-are-consumer-reviews-good-for-business ( accessed on July 4, 2018).
   Google Scholar
• Bockstedt, J.; and Goh, K. H. Seller strategies for differentiation in highly competitive online auction markets. Journal of Management Information Systems, 28, 3 (2011), 235–268.
   Web of Science ®Google Scholar
• Breiman, L. Bagging predictors. Machine Learning, 24, 2 (1996), 123–140.
   Web of Science ®Google Scholar
• Chandola, V.; Banerjee, A.; and Kumar, V. Anomaly detection: A survey. ACM Computing Surveys, 41, 3 (2009), 15:1-15:58.
   Google Scholar
• Chevalier, J.A.; and Mayzlin, D. The effect of word of mouth on sales: Online book reviews. Journal of Marketing Research, 43, 3 (2006), 345–354.
   Web of Science ®Google Scholar
• Chou, L.; Sarkhel, S.; Ruozzi, N.; and Gogate, V. On parameter tying by quantization. In Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence, 2016, pp. 3241–3247.
   Google Scholar
• Churilov, L.; Bagirov, A.; Schwartz, D.; Smith, K.; and Dally, M. Data mining with combined use of optimization techniques and self-organizing maps for improving risk grouping rules: application to prostate cancer patients. Journal of Management Information Systems, 21, 4 (2005), 85–100.
   Web of Science ®Google Scholar
• ClearMyMail. Viagra spam e-mails.http://www.clearmymail.com/guides/viagra_spam_emails.aspx ( accessedon July 4, 2018).
   Google Scholar
• Cormack, G.V. Email spam filtering: A systematic review. Foundations and Trends in Information Retrieval, 1, 4 (2008), 335–455.
   Google Scholar
• Dalvi, N.N.; Kumar, R.; and Pang, B. Para ’normal’ activity: On the distribution of average ratings. Proceedings of the International AAAI Conference on Weblogs and Social Media, 7, (2013), 110–119.
   Google Scholar
• Eliashberg, J.; and Shugan, S.M. Film critics: Influencers or predictors? Journal of Marketing, 61, 2 (1997), 68–78.
   Web of Science ®Google Scholar
• Elkan, C. Clustering documents with an exponential-family approximation of the dirichlet compound multinomial distribution. In Proceedings of the 23rd International Conference on Machine Learning. New York, NY: ACM, 2006, pp. 289–296.
   Google Scholar
• Eskin, E. Anomaly detection over noisy data using learned probability distributions. In Proceedings of the Seventeenth International Conference on Machine Learning. San Francisco, CA: Morgan Kaufmann, 2000, pp. 255–262.
   Google Scholar
• 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.
   Google Scholar
• 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.
   Google Scholar
• Feng, S.; Xing, L.; Gogar, A.; and Choi, Y. Distributional footprints of deceptive product reviews. Proceedings of the International AAAI Conference on Weblogs and Social Media, 6, (2012), 98–105.
   Google Scholar
• Ferrara, E.; Varol, O.; Davis, C.; Menczer, F.; and Flammini, A. The rise of social bots. Communications of the ACM, 59, 7 (2016), 96–104.
   Web of Science ®Google Scholar
• 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.
   Web of Science ®Google Scholar
• Freund, Y.; and Schapire, R.E. Experiments with a new boosting algorithm. In Proceedings of the Thirteenth International Conference on Machine Learning. San Francisco, CA: Morgan Kaufmann, 1996, pp. 148–156.
   Google Scholar
• Gogate, V.; Webb, W.; and Domingos, P. Learning efficient markov networks. In Advances in Neural Information Processing Systems, 2010, pp. 748–756.
   Google Scholar
• Guo, J.; Zhang, W.; Fan, W.; and Li, W. Combining geographical and social influences with deep learning for personalized point-of-interest recommendation. Journal of Management Information Systems, 35, 4 (2018), 1121–1153.
   Web of Science ®Google Scholar
• Heller, K.A.; Svore, K.M.; Keromytis, A.D.; and Stolfo, S.J. One class support vector machines for detecting anomalous windows registry accesses. In Proceedings of the workshop on Data Mining for Computer Security, 2003.
   Google Scholar
• 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.
   Web of Science ®Google Scholar
• Holmes, I.; Harris, K.; and Quince, C. Dirichlet multinomial mixtures: Generative models for microbial metagenomics. PLoS ONE, 7, 2 (2012), 1–15.
   Web of Science ®Google Scholar
• 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.
   Web of Science ®Google Scholar
• Hu, N.; Bose, I.; Koh, N.S.; and Liu, L. Manipulation of online reviews: An analysis of ratings, readability, and sentiments. Decision Support Systems, 52, 3 (2012), 674–684.
   Web of Science ®Google Scholar
• 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.
   Web of Science ®Google Scholar
• Hulten, G.; Spencer, L.; and Domingos, P. Mining time-changing data streams. In Proceedings of the Seventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York, NY: ACM, 2001, pp. 97–106.
   Google Scholar
• Ivanova, O.; and Scholz, M. How can online marketplaces reduce rating manipulation? A new approach on dynamic aggregation of online ratings. Decision Support Systems, 104, 4 (2017), 64–78.
   Web of Science ®Google Scholar
• Jindal, N.; and Liu, B. Opinion spam and analysis. In Proceedings of the 2008 International Conference on Web Search and Data Mining, New York, NY: ACM, 2008, pp. 219–230.
   Google Scholar
• Jindal, N.; Liu, B.; and Lim, E.-P. Finding unusual review patterns using unexpected rules. In Proceedings of the 19th ACM International Conference on Information and Knowledge Management. New York, NY: ACM, 2010, pp. 1549–1552.
   Google Scholar
• Kumar, N.; Venugopal, D.; Qiu, L.; and Kumar, S. Detecting review manipulation on online platforms with hierarchical supervised learning. Journal of Management Information Systems, 35, 1 (2018), 350–380.
   Web of Science ®Google Scholar
• Kurtzleben, D. Did fake news on Facebook help elect Trump? Here’s what we know. NPR News. April 11, 2018. https://www.npr.org/2018/04/11/601323233/6-facts-we-know-about-fake-news-in-the-2016-election ( accessed on July 4, 2018).
   Google Scholar
• 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.
   Web of Science ®Google Scholar
• 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. ACM Transactions on Management Information Systems, 2, 4 (2011), 1–30.
   Google Scholar
• Lazer, D.M.; Baum, M.A.; Benkler, Y.; Berinsky, A.J.; Greenhill, K.M.; Menczer, F.; and Schudson, M. The science of fake news. Science, 359, 6380 (2018), 1094–1096.
   PubMed Web of Science ®Google Scholar
• Lee, S.Y.; Qiu, L.; and Whinston, A.B. Sentiment manipulation in online platforms: An analysis of movie tweets. Production and Operations Management, 27, 3 (2018), 393–416.
   Web of Science ®Google Scholar
• 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.
   Google Scholar
• Li, J.; Ott, M.; Cardie, C.; and Hovy, E. Towards a general rule for identifying deceptive opinion spam. Proceedings of the Annual Meeting of the Association for Computational Linguistics, 52, (2014), 1566–1576.
   Google Scholar
• 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.
   Google Scholar
• Lowd, D.; and Rooshenas, A. Learning markov networks with arithmetic circuits. In Artificial Intelligence and Statistics. 2013, pp. 406–414.
   Google Scholar
• Luca, M.; and Zervas, G. Fake it till you make it: reputation, competition, and Yelp review fraud. Management Science, 62, 12 (2016), 3412–3427.
   Web of Science ®Google Scholar
• 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.
   Web of Science ®Google Scholar
• Mahmoud, H. Polya Urn Models (1st ed.). New York, NY: Chapman and Hall/CRC, 2008.
   Google Scholar
• Manning, C.D.; Raghavan, P.; and Schütze, H. Introduction to Information Retrieval. New York, NY: Cambridge University Press, 2008.
   Google Scholar
• Mayzlin, D. Promotional chat on the Internet. Marketing Science, 25, 2 (2006), 155–163.
   Web of Science ®Google Scholar
• Mayzlin, D.; Dover, Y.; and Chevalier, J. Promotional reviews: an empirical investigation of online review manipulation. American Economic Review, 104, 8 (2014), 2421–2455.
   Web of Science ®Google Scholar
• Michaels, J. Four digital marketing trends that will impact small business in 2015. Beyond Social Buzz, 2014. https://beyondsocialbuzz.co.uk/small-business-digital-marketing-trends/( accessed on July 4, 2018).
   Google Scholar
• Mimno, D.M.; and McCallum, A. Topic models conditioned on arbitrary features with Dirichlet-multinomial regression. In Proceedings of the Twenty-Fourth Conference on Uncertainty in Artificial Intelligence. Arlington, VA: AUAI Press, 2008, pp. 411–418.
   Google Scholar
• Minka, T. Estimating a Dirichlet distribution. Technical report, MIT, 2000.
   Google Scholar
• Mintel. Seven in 10 Americans seek out opinions before making purchases. Mintel. June 3, 2015. http://www.mintel.com/press-centre/social-and-lifestyle/seven-in-10-americans-seek-out-opinions-before-making-purchases. ( accessed on July 4, 2018).
   Google Scholar
• 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.
   Google Scholar
• 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.
   Google Scholar
• 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.
   Google Scholar
• Narayan, R.; Rout, J. K.; and Jena, S. K. Review spam detection using opinion mining. In Progress in Intelligent Computing Techniques: Theory, Practice, and Applications. Springer, Singapore, 2018, pp. 273–279.
   Google Scholar
• Newman, M.L.; Pennebaker, J.W.; Berry, D.S.; and Richards, J.M. Lying words: Predicting deception from linguistic styles. Personality and Social Psychology Bulletin, 29, 5 (2003), 665–675.
   PubMed Web of Science ®Google Scholar
• Ott, M.; Cardie, C.; and Hancock, J.T. Estimating the prevalence of deception in online review communities. Proceedings of the International Conference on World Wide Web, 21, (2012), 201–210.
   Google Scholar
• 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.
   Google Scholar
• Pang, B.; and Lee, L. Opinion mining and sentiment analysis. Foundations and Trends in Information Retrieval, 2, 1–2 (2008), 1–135.
   Google Scholar
• Park, S.; Shin, W.; and Xie, J. The first-review effect: Interdependence between volume and valence of online consumer reviews. Working paper, University of Florida, 2016. https://ssrn.com/abstract=2824846 ( accessed on July 4, 2018).
   Google Scholar
• Pearl, J. Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. San Francisco, CA: Morgan Kaufmann, 1988.
   Google Scholar
• 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.
   Web of Science ®Google Scholar
• Qiu, L.; Tang, Q.; and Whinston, A.B. Two formulas for success in social media: Learning and network effects. Journal of Management Information Systems, 32, 4 (2015), 78–108.
   Web of Science ®Google Scholar
• Olivieri, A.; Shabani, S.; Sokhn, M.; and Cudré-Mauroux, P. Creating task-generic features for fake news detection. In Proceedings of the 52nd Hawaii International Conference on System Sciences, 2019, pp. 5196–5205.
   Google Scholar
• 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.
   Google Scholar
• Roberts, 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 July 4, 2018).
   Google Scholar
• Rooshenas, A.; and Lowd, D. Learning sum-product networks with direct and indirect variable interactions. In International Conference on Machine Learning, (2014), pp. 710–718.
   Google Scholar
• Rudolph, S. The impact of online reviews on customers buying decisions. Business 2 Community. July 25, 2015. https://www.business2community.com/infographics/impact-online-reviews-customers-buying-decisions-infographic-01280945#iZwM69pSgVKLlH6A.97 ( accessed on July 4, 2018).
   Google Scholar
• Sarkhel, S.; Venugopal, D.; Pham, T. A.; Singla, P.; and Gogate, V. Scalable training of markov logic networks using approximate counting. In Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence, 2016, pp. 1067–1073.
   Google Scholar
• Schölkopf, B.; Platt, J.C.; Shawe-Taylor, J.C.; Smola, A.J.; and Williamson, R.C. Estimating the support of a high-dimensional distribution. Neural Computation, 13, 7 (2001), 1443–1471.
   PubMed Web of Science ®Google Scholar
• Schölkopf, B.; Williamson, R.C.; Smola, A.J.; Shawe-Taylor, J.; and Platt, J.C. Support vector method for novelty detection. Proceedings of Advances in Neural Information Processing Systems, 12, (1999), 582–588.
   Google Scholar
• Siering, M.; Koch, J.A.; and Deokar, A.V. Detecting fraudulent behavior on crowdfunding platforms: the role of linguistic and content-based cues in static and dynamic contexts. Journal of Management Information Systems, 33, 2 (2016), 421–455.
   Web of Science ®Google Scholar
• Smyth, P.; and Wolpert, D. Linearly combining density estimators via stacking. Machine Learning, 36, 1 (1999), 59–83.
   Web of Science ®Google Scholar
• Smyth, P.; and Wolpert, D. Stacked density estimation. Proceedings of Advances in Neural Information Processing Systems, 10, (1997), 668–674.
   Google Scholar
• Song, X.; Wu, M.; Jermaine, C.; and Ranka, S. Conditional anomaly detection. IEEE Transactions on Knowledge and Data Engineering, 19, 5 (2007), 631–645.
   Web of Science ®Google Scholar
• Stolfo, S.J.; Apap, F.; Eskin, E.; Heller, K.; Hershkop, S.; Honig, A.; and Svore, K. A comparative evaluation of two algorithms for windows registry anomaly detection. Journal of Computer Security, 13, 4 (2005), 659–693.
   Google Scholar
• 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 July 4, 2018).
   Google Scholar
• Susarla, A.; Oh, J.H.; and Tan, Y. Influentials, imitables, or susceptibles? Virality and word-of-mouth conversations in online social networks. Journal of Management Information Systems, 33, 1 (2016), 139–170.
   Web of Science ®Google Scholar
• Susarla, A.; Oh, J.H.; and Tan, Y. Social networks and the diffusion of user-generated content: Evidence from YouTube. Information Systems Research, 23, 1 (2012), 23–41.
   Web of Science ®Google Scholar
• Visa, A.; Toivonen, J.; Vanharanta, H.; and Back, B. Contents matching defined by prototypes: Methodology verification with books of the Bible. Journal of Management Information Systems, 18, 4 (2002), 87–100.
   Web of Science ®Google Scholar
• Vosoughi, S.; Roy, D.; and Aral, S. The spread of true and false news online. Science, 359, 6380 (2018), 1146–1151.
   PubMed Web of Science ®Google Scholar
• Wang, G.; Xie, S.; Liu, B.; and Yu, P.S. Review graph based online store review spammer detection. Proceedings of the International Conference on Data Mining, 11, (2011), 1242–1247.
   Google Scholar
• Wolpert, D. Stacked generalization. Neural Networks, 5, 2 (1992), 241–259.
   Web of Science ®Google Scholar
• Xie, S.; Wang, G.; Lin, S.; and Yu, P.S. Review spam detection via temporal pattern discovery. Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 18, (2012), 823–831.
   Google Scholar
• Xu, C.; and Zhang, J. Towards collusive fraud detection in online reviews. Proceedings of IEEE International Conference on Data Mining, 15, (2015), 1051–1056.
   Google Scholar
• 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.
   Google Scholar
• Ye, J.; Kumar, S.; and Akoglu, L. Temporal opinion spam detection by multivariate indicative signals. Proceedings of the International AAAI Conference on Web and Social Media, 10, (2016), 743–746.
   Google Scholar
• Yedidia, J.S.; Freeman, W.T.; and Weiss, Y. Constructing free-energy approximations and generalized Belief propagation algorithms. IEEE Transactions on Information Theory, 51, 7 (2005), 2282–2312.
   Web of Science ®Google Scholar
• Yerazunis, W.S. The spam-filtering accuracy plateau at 99.9% accuracy and how to get past it. Proceedings of MIT Spam Conference, 2004.
   Google Scholar
• 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.
   Web of Science ®Google Scholar
• Zheng, X.; Zhu, S.; and Lin, Z. Capturing the essence of word-of-mouth for social commerce: Assessing the quality of online e-commerce reviews by a semi-supervised approach. Decision Support Systems, 56, 4 (2013), 211–222.
   Web of Science ®Google Scholar
• Zhou, L.; Shi, Y.; and Zhang, D. A statistical language modeling approach to online deception detection. IEEE Transactions on Knowledge and Data Engineering, 20, 8 (2008), 1077–1081.
   Web of Science ®Google Scholar