Michael Fire
ABSTRACT
User feedback is one of the most effective methods to build and maintain trust in electronic commerce platforms. Unfortunately, dishonest sellers often bend over backward to manipulate users’ feedback or place phony bids in order to increase their own sales and harm competitors. The black market of user feedback, supported by a plethora of shill bidders, prospers on top of legitimate electronic commerce. In this paper, we investigate the ecosystem of shill bidders based on large-scale data by analyzing hundreds of millions of users who performed billions of transactions, and we propose a machine-learning-based method for identifying communities of users that methodically provide dishonest feedback. Our results show that (1) shill bidders can be identified with high precision based on their transaction and feedback statistics; and (2) in contrast to legitimate buyers and sellers, shill bidders form cliques to support each other.
- 2012. What is Shill Bidding?https://support.flippa.com/hc/en-us/articles/202469674-What-is-Shill-Bidding-. (Accessed on 10/01/2023).Google Scholar
- 2016. I Get Paid To Write Fake Reviews For Amazon. http://www.cracked.com/personal-experiences-2376-i-get-paid-to-write-fake-reviews-amazon.html. (Accessed on 10/06/2022).Google Scholar
- 2018. Facebook Will Ban Sellers of Shoddy Products. https://www.wsj.com/articles/facebook-will-ban-sellers-of-shoddy-products-1528794000. (Accessed on 10/06/2022).Google Scholar
- 2018. There Are 168 Million Active Buyers on eBay Right Now (INFOGRAPHIC) - Small Business Trends. https://smallbiztrends.com/2018/03/ebay-statistics-march-2018.html. (Accessed on 10/07/2022).Google Scholar
- 2019. Global Ecommerce 2019 - eMarketer Trends, Forecasts & Statistics. https://www.emarketer.com/content/global-ecommerce-2019. (Accessed on 03/03/2022).Google Scholar
- 2020. The True Cost of E-Commerce Fraud. https://blog.clear.sale/the-true-cost-of-e-commerce-fraud. (Accessed on 05/01/2023).Google Scholar
- 2021. Alibaba Group Announces March Quarter and Full Fiscal Year 2021 Results. https://www.sec.gov/Archives/edgar/data/1577552/000110465921065916/tm2116252d1_ex99-1.htm. (Accessed on 10/01/2023).Google Scholar
- 2021. Facebook - Financials - SEC Filings Details. https://investor.fb.com/financials/sec-filings-details/default.aspx?FilingId=15030787. (Accessed on 10/01/2023).Google Scholar
- 2022. Retail e-commerce sales worldwide from 2014 to 2026. https://www.statista.com/statistics/379046/worldwide-retail-e-commerce-sales/. (Accessed on 10/06/2022).Google Scholar
- 2022. Shill Bidding. https://www.nyccriminallawyer.com/fraud-charge/auction-fraud/shill-bidding/. (Accessed on 10/01/2023).Google Scholar
- Amazon. [n. d.]. Amazon Global Selling, Sell & Ship Products Internationally - Amazon. https://sell.amazon.com/global-selling.html. (Accessed on 10/01/2023).Google Scholar
- Yordanos Beyene, Michalis Faloutsos, Duen Horng Chau, and Christos Faloutsos. 2008. The eBay Graph: How do online auction users interact?. In INFOCOM Workshops 2008, IEEE. IEEE, 1–6.Google ScholarCross Ref
- Richard Black. 1994. Fast CRC32 in software. ATM Document Collection 3 (1994).Google Scholar
- Coen Bron and Joep Kerbosch. 1973. Algorithm 457: finding all cliques of an undirected graph. Commun. ACM 16, 9 (1973), 575–577.Google ScholarDigital Library
- Indranil Chakraborty and Georgia Kosmopoulou. 2004. Auctions with shill bidding. Economic Theory 24, 2 (2004), 271–287.Google ScholarCross Ref
- Wen-Hsi Chang and Jau-Shien Chang. 2011. A novel two-stage phased modeling framework for early fraud detection in online auctions. Expert Systems with Applications 38, 9 (2011), 11244 – 11260. https://doi.org/10.1016/j.eswa.2011.02.172Google ScholarDigital Library
- Duen Horng Chau and Christos Faloutsos. 2005. Fraud detection in electronic auction. In European Web Mining Forum at ECML/PKDD. 87–97.Google Scholar
- Gabor Csardi and Tamas Nepusz. 2006. The igraph software package for complex network research. InterJournal, Complex Systems 1695, 5 (2006).Google Scholar
- eBay. 2022. Shill bidding policy. http://pages.ebay.com/help/policies/seller-shill-bidding.html. (Accessed on 10/06/2022).Google Scholar
- Michael Fire, Dima Kagan, Aviad Elyashar, and Yuval Elovici. 2014. Friend or foe? Fake profile identification in online social networks. Social Network Analysis and Mining 4, 1 (2014), 194.Google ScholarCross Ref
- Swati Ganguly and Samira Sadaoui. 2018. Online Detection of Shill Bidding Fraud Based on Machine Learning Techniques. In International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems. Springer, 303–314.Google Scholar
- M. Hall, E. Frank, G. Holmes, B. Pfahringer, P. Reutemann, and I.H. Witten. 2009. The WEKA data mining software: an update. ACM SIGKDD Explorations Newsletter 11, 1 (2009), 10–18.Google ScholarDigital Library
- Chuck Jones. 2013. Ecommerce Is Growing Nicely While Mcommerce Is On A Tear. Forbes (October 2013). http://www.forbes.com/sites/chuckjones/2013/10/02/ecommerce-is-growing-nicely-while-mcommerce-is-on-a-tear/ [Online; accessed 10/06/2022].Google Scholar
- Patric Kabus, Wesley W. Terpstra, Mariano Cilia, and Alejandro P. Buchmann. 2005. Addressing Cheating in Distributed MMOGs. In Proceedings of 4th ACM SIGCOMM Workshop on Network and System Support for Games (Hawthorne, NY) (NetGames ’05). ACM, New York, NY, USA, 1–6. https://doi.org/10.1145/1103599.1103607Google ScholarDigital Library
- Parisa Kaghazgaran, James Caverlee, and Majid Alfifi. 2017. Behavioral Analysis of Review Fraud: Linking Malicious Crowdsourcing to Amazon and Beyond.. In ICWSM. 560–563.Google Scholar
- Robert J Kauffman and Charles A Wood. 2003. Running up the bid: detecting, predicting, and preventing reserve price shilling in online auctions. In Proceedings of the 5th international conference on Electronic commerce. ACM, 259–265.Google ScholarDigital Library
- Jure Leskovec, Daniel Huttenlocher, and Jon Kleinberg. 2010. Predicting positive and negative links in online social networks. In Proceedings of the 19th international conference on World wide web. ACM, 641–650.Google ScholarDigital Library
- B.N. Levine, C. Shields, and N.B. Margolin. 2006. A survey of solutions to the sybil attack. University of Massachusetts Amherst, Amherst, MA (2006).Google Scholar
- [29] Joshua Lockhart. [n. d.]. http://www.makeuseof.com/tag/can-you-really-win-almost-any-ebay-auction-by-sniping/. [Online; accessed 10/06/2022].Google Scholar
- David Lucking-Reiley, Doug Bryan, Naghi Prasad, and Daniel Reeves. 2007. Pennies from Ebay: The Determinants of Price in Online Auctions. The Journal of Industrial Economics 55, 2 (2007), 223–233.Google ScholarCross Ref
- Nazia Majadi, Jarrod Trevathan, and Neil Bergmann. 2016. Analysis on Bidding Behaviours for Detecting Shill Bidders in Online Auctions. In Computer and Information Technology (CIT), 2016 IEEE International Conference on. IEEE, 383–390.Google ScholarCross Ref
- Jason Mccormick. 2012. 10 most expensive items ever listed on eBay - CBS News. https://www.cbsnews.com/media/10-most-expensive-items-ever-listed-on-ebay. (Accessed on 10/06/2022).Google Scholar
- Susan M Mudambi and David Schuff. 2010. What makes a helpful online review? A study of customer reviews on Amazon. com. MIS quarterly 34, 1 (2010), 185–200.Google Scholar
- Richard Harris Nicholas Confessore, Gabriel J.X. Dance and Mark Hansen. 2018. The Follower Factory - The New York Times. https://www.nytimes.com/interactive/2018/01/27/technology/social-media-bots.html. (Accessed on 10/06/2022).Google Scholar
- Shashank Pandit, Duen Horng Chau, Samuel Wang, and Christos Faloutsos. 2007. Netprobe: a fast and scalable system for fraud detection in online auction networks. In Proceedings of the 16th international conference on World Wide Web. ACM, 201–210.Google ScholarDigital Library
- Md Sazzadur Rahman, Ting-Kai Huang, Harsha V Madhyastha, and Michalis Faloutsos. 2012. FRAppE: detecting malicious facebook applications. In Proceedings of the 8th international conference on Emerging networking experiments and technologies. ACM, 313–324.Google ScholarDigital Library
- Paul Resnick, Ko Kuwabara, Richard Zeckhauser, and Eric Friedman. 2000. Reputation systems. Commun. ACM 43, 12 (2000), 45–48.Google ScholarDigital Library
- Alvin E Roth and Axel Ockenfels. 2000. Last minute bidding and the rules for ending second-price auctions: Theory and evidence from a natural experiment on the Internet. Technical Report. National bureau of economic research.Google Scholar
- Statista. [n. d.]. Number of eBay’s total active buyers from 1st quarter 2010 to 2nd quarter 2022. https://www.statista.com/statistics/242235/number-of-ebays-total-active-users/. (Accessed on 10/01/2023).Google Scholar
- [40] Techopedia.com. [n. d.]. http://www.techopedia.com/definition/27959/auction-sniping/. [Online; accessed 10/06/2022].Google Scholar
- Jarrod Trevathan and Wayne Read. 2009. Detecting shill bidding in online English auctions. Handbook of research on social and organizational liabilities in information security (2009), 446–470.Google Scholar
- Emma Woollacott. 2017. Amazon’s Fake Review Problem Is Now Worse Than Ever, Study Suggests. https://www.forbes.com/sites/emmawoollacott/2017/09/09/exclusive-amazons-fake-review-problem-is-now-worse-than-ever/. (Accessed on 10/1/2023).Google Scholar
- Yu Zhang, Jing Bian, and Weixiang Zhu. 2013. Trust fraud: A crucial challenge for China’s e-commerce market. Electronic Commerce Research and Applications 12, 5 (2013), 299 – 308. https://doi.org/10.1016/j.elerap.2012.11.005 Chinese E-Commerce.Google ScholarDigital Library
- Shi Zhou and Raúl J Mondragón. 2004. The rich-club phenomenon in the Internet topology. Communications Letters, IEEE 8, 3 (2004), 180–182.Google ScholarCross Ref
Index Terms
- Large-Scale Shill Bidder Detection in E-commerce
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