  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.

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