Large-Scale Shill Bidder Detection in E-commerce

TL;DR

This paper presents a large-scale analysis of shill bidders in e-commerce, introducing a machine learning method to identify dishonest user communities that manipulate feedback and sales.

Contribution

It proposes a novel machine learning approach to detect shill bidder communities using transaction and feedback data at scale.

Findings

Shill bidders can be identified with high precision.

Shill bidders tend to form cliques to support each other.

The method effectively distinguishes dishonest from legitimate users.

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.