Are Neighbors Alike? A Semisupervised Probabilistic Collaborative Learning Model for Online Review Spammers Detection

Published Online:https://doi.org/10.1287/isre.2022.0047

References

  • Abbasi A, Zahedi FM, Kaza S (2012) Detecting fake medical web sites using recursive trust labeling. ACM Trans. Inform. Systems 30(4):1–36.CrossrefGoogle Scholar
  • Abbasi A, Zhang Z, Zimbra D, Chen H, Nunamaker JF Jr (2010) Detecting fake websites: The contribution of statistical learning theory. MIS Quart. 34(3):435–461.CrossrefGoogle Scholar
  • Amazon (2021) Creating a trustworthy reviews experience. Accessed January 1, 2022, https://www.aboutamazon.com/news/how-amazon-works/creating-a-trustworthy-reviews-experience.Google Scholar
  • Bauman K, Tuzhilin A (2022) Know thy context: Parsing contextual information from user reviews for recommendation purposes. Inform. Systems Res. 33(1):179–202.LinkGoogle Scholar
  • Blondel VD, Guillaume JL, Lambiotte R, Lefebvre E (2008) Fast unfolding of communities in large networks. J. Statist. Mech. Theory Experiment 2008(10):P10008.CrossrefGoogle Scholar
  • Chen H, Liu J, Lv Y, Li MH, Liu M, Zheng Q (2018) Semi-supervised clue fusion for spammer detection in Sina Weibo. Inform. Fusion 44:22–32.CrossrefGoogle Scholar
  • Chirita P, Nejdl W, Zamfir C (2005) Preventing shilling attacks in online recommender systems. Proc. 7th Annual ACM Internat. Workshop Web Inform. Data Management (Association for Computing Machinery, New York), 67–74.Google Scholar
  • Dang Q, Zhou Y, Gao F, Sun Q (2017) Detecting cooperative and organized spammer groups in micro-blogging community. Data Mining Knowledge Discovery 31(3):573–605.CrossrefGoogle Scholar
  • Dellarocas C (2006) Strategic manipulation of internet opinion forums: Implications for consumers and firms. Management Sci. 52(10):1577–1593.LinkGoogle Scholar
  • Dhar V, Geva T, Oestreicher-Singer G, Sundararajan A (2014) Prediction in economic networks. Inform. Systems Res. 25(2):264–284.LinkGoogle Scholar
  • Fakhraei S, Foulds J, Shashanka M, Getoor L (2015) Collective spammer detection in evolving multi-relational social networks. Proc. 21th ACM SIGKDD Internat. Conf. Knowledge Discovery Data Mining (Association for Computing Machinery, New York), 1769–1778.Google Scholar
  • Fayazi A, Lee K, Caverlee J, Squicciarini A (2015) Uncovering crowdsourced manipulation of online reviews. Proc. 38th Internat. ACM SIGIR Conf. Res. Development Inform. Retrieval, 233–242.Google Scholar
  • Gao GG, Greenwood BN, Agarwal R, McCullough JS (2015) Vocal minority and silent majority: How do online ratings reflect population perceptions of quality? MIS Quart. 39(3):565–589.CrossrefGoogle Scholar
  • Ho YC, Wu J, Tan Y (2017) Disconfirmation effect on online rating behavior: A structural model. Inform. Systems Res. 28(3):626–642.LinkGoogle Scholar
  • Jiang Y, Guo H (2015) Design of consumer review systems and product pricing. Inform. Systems Res. 26(4):714–730.LinkGoogle Scholar
  • Jindal N, Liu B (2008) Opinion spam and analysis. Proc. First Internat. Conf. Web Search Data Mining (Association for Computing Machinery, New York), 219–230.Google Scholar
  • Kingma DP, Ba J (2014) Adam: A method for stochastic optimization. Preprint, submitted December 22, https://arxiv.org/abs/1412.6980.Google Scholar
  • Krishnan V, Raj R (2006) Web spam detection with anti-trust rank. Proc. Second Internat. Workshop Adversarial Inform. Retrieval Web (Lehigh University, Bethlehem, PA).Google Scholar
  • Kumar N, Venugopal D, Qiu L, Kumar S (2018) Detecting review manipulation on online platforms with hierarchical supervised learning. J. Management Inform. Systems 35(1):350–380.CrossrefGoogle Scholar
  • Kumar N, Venugopal D, Qiu L, Kumar S (2019) Detecting anomalous online reviewers: An unsupervised approach using mixture models. J. Management Inform. Systems 36(4):1313–1346.CrossrefGoogle Scholar
  • Kwark Y, Chen J, Raghunathan S (2014) Online product reviews: Implications for retailers and competing manufacturers. Inform. Systems Res. 25(1):93–110.LinkGoogle Scholar
  • Lam S, Riedl J (2004) Shilling recommender systems for fun and profit. Proc. 13th Internat. Conf. World Wide Web (Association for Computing Machinery, New York), 393–402.Google Scholar
  • Lappas T, Sabnis G, Valkanas G (2016) The impact of fake reviews on online visibility: A vulnerability assessment of the hotel industry. Inform. Systems Res. 27(4):940–961.LinkGoogle Scholar
  • Lee JS, Zhu D (2012) Shilling attack detection—A new approach for a trustworthy recommender system. INFORMS J. Comput. 24(1):117–131.LinkGoogle Scholar
  • Li F, Huang M, Yang Y, Zhu X (2011) Learning to identify review spam. Walsh T, ed. Proc. 22nd Internat. Joint Conf. Artificial Intelligence (AAAI Press, Palo Alto, CA), 2488–2493.Google Scholar
  • Liu Y, Pant G, Sheng OR (2020) Predicting labor market competition: Leveraging interfirm network and employee skills. Inform. Systems Res. 31(4):1443–1466.LinkGoogle Scholar
  • Luca M, Zervas G (2016) Fake it till you make it: Reputation, competition, and yelp review fraud. Management Sci. 62(12):3412–3427.LinkGoogle Scholar
  • Ma X, Wu J, Xue S, Yang J, Zhou C, Sheng QZ, Xiong H, Akoglu L (2021) A comprehensive survey on graph anomaly detection with deep learning. IEEE Trans. Knowledge Data Engrg., ePub ahead of print October 8, https://ieeexplore.ieee.org/abstract/document/9565320.CrossrefGoogle Scholar
  • Mobasher B, Burke R, Sandvig J (2006) Model-based collaborative filtering as a defense against profile injection attacks. Proc. 21th AAAI Conf. Artificial Intelligence (AAAI Press, Palo Alto, CA), 1388–1393.Google Scholar
  • Mukherjee A, Venkataraman V, Liu B, Glance NS (2013a) What Yelp fake review filter might be doing? Proc. 7th Internat. Conf. Weblogs Social Media, vol. 7 (AAAI Press, Palo Alto, CA), 409–418.Google Scholar
  • Mukherjee A, Kumar A, Liu B, Wang J, Hsu M, Castellanos M, Ghosh R (2013b) Spotting opinion spammers using behavioral footprints. Proc. 19th ACM SIGKDD Internat. Conf. Knowledge Discovery Data Mining (Association for Computing Machinery, New York), 632–640.Google Scholar
  • Paul H, Nikolaev A (2021) Fake review detection on online e-commerce platforms: A systematic literature review. Data Mining Knowledge Discovery 35(5):1830–1881.CrossrefGoogle Scholar
  • Perozzi B, Al-Rfou R, Skiena S (2014) DeepWalk: Online learning of social representations. Proc. 20th ACM SIGKDD Internat. Conf. Knowledge Discovery Data Mining (Association for Computing Machinery, New York), 701–710.Google Scholar
  • Rayana S, Akoglu L (2015) Collective opinion spam detection: Bridging review networks and metadata. Proc. 21th ACM SIGKDD Internat. Conf. Knowledge Discovery Data Mining (Association for Computing Machinery, New York), 985–994.Google Scholar
  • Shehnepoor S, Salehi M, Farahbakhsh R, Crespi N (2017) Netspam: A network-based spam detection framework for reviews in online social media. IEEE Trans. Inform. Forensics Security 12(7):1585–1595.CrossrefGoogle Scholar
  • Sun T, Viswanathan S, Zheleva E (2021) Creating social contagion through firm-mediated message design: Evidence from a randomized field experiment. Management Sci. 67(2):808–827.LinkGoogle Scholar
  • Wang G, Xie S, Liu B, Yu PS (2012) Identify online store review spammers via social review graph. ACM Trans. Intelligent Systems Tech. 3(4):61.CrossrefGoogle Scholar
  • Wang Z, Hu R, Chen Q, Gao P, Xu X (2020) ColluEagle: Collusive review spammer detection using Markov random fields. Data Mining Knowledge Discovery 34(6):1621–1641.CrossrefGoogle Scholar
  • Welling M, Kipf TN (2017) Semi-supervised classification with graph convolutional networks. Internat. Conf. Learn. Representations (ICLR 2017).Google Scholar
  • Williams C, Mobasher B (2006) Profile injection attack detection for securing collaborative recommender systems. DePaul University CTI Technical Report, 1–47.Google Scholar
  • Wu B, Davison BD (2005) Identifying link farm spam pages. 14th Internat. Conf. World Wide Web (Association for Computing Machinery, New York), 820–829.Google Scholar
  • Wu Y, Ngai EW, Wu P, Wu C (2020a) Fake online reviews: Literature review, synthesis, and directions for future research. Decision Support Systems 132:113280.CrossrefGoogle Scholar
  • Wu Y, Lian D, Xu Y, Wu L, Chen E (2020b) Graph convolutional networks with Markov random field reasoning for social spammer detection. Proc. Conf. AAAI Artificial Intelligence, vol. 34 (AAAI Press, Palo Alto, CA), 1054–1061.Google Scholar
  • Wu Z, Wu J, Cao J, Tao D (2012) HySAD: A semi-supervised hybrid shilling attack detector for trustworthy product recommendation. Proc. 18th ACM SIGKDD Internat. Conf. Knowledge Discovery Data Mining (Association for Computing Machinery, New York), 985–993.Google Scholar
  • Wu Z, Cao J, Wang Y, Wang Y, Zhang L, Wu J (2020c) hPSD: A hybrid PU-learning-based spammer detection model for product reviews. IEEE Trans. Cybernetics 50(4):1595–1606.CrossrefGoogle Scholar
  • Xu H, Liu D, Wang H, Stavrou A (2017) An empirical investigation of ecommerce-reputation-escalation-as-a-service. ACM Trans. Web 11(2):1–35.CrossrefGoogle Scholar
  • Yi C, Jiang Z, Li X, Lu X (2019) Leveraging user-generated content for product promotion: The effects of firm-highlighted reviews. Inform. Systems Res. 30(3):711–725.LinkGoogle Scholar
  • Yin D, Bond SD, Zhang H (2014) Anxious or angry? Effects of discrete emotions on the perceived helpfulness of online reviews. MIS Quart. 38:539–560.CrossrefGoogle Scholar
  • Zhang D, Zhou L, Kehoe JL, Kilic IY (2016) What online reviewer behaviors really matter? Effects of verbal and nonverbal behaviors on detection of fake online reviews. J. Management Inform. Systems 33(2):456–481.CrossrefGoogle Scholar
  • Zhang L, Zhang Y, Zhang Y, Li X (2006) Exploring both content and link quality for anti-spamming. Sixth IEEE Internat. Conf. Comput. Inform. Tech. (CIT’06) (IEEE, Piscataway, NJ).Google Scholar
  • Zhou T, Wang Y, Yan L(L), Tan Y (2023) Spoiled for choice? Personalized recommendation for healthcare decisions: A multiarmed bandit approach. Inform. Systems Res., ePub ahead of print January 19, https://doi.org/10.1287/isre.2022.1191.LinkGoogle Scholar
  • Zhu F, Zhang X (2010) Impact of online consumer reviews on sales: The moderating role of product and consumer characteristics. J. Marketing 74(2):133–148.CrossrefGoogle Scholar
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