Information Retrieval
Labeling images with a computer game
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Secure distributed human computation
Proceedings of the 6th ACM conference on Electronic commerce
Fraudulent auctions on the Internet
Electronic Commerce Research
A survey of trust and reputation systems for online service provision
Decision Support Systems
Netprobe: a fast and scalable system for fraud detection in online auction networks
Proceedings of the 16th international conference on World Wide Web
Internet-scale collection of human-reviewed data
Proceedings of the 16th international conference on World Wide Web
The Role of Reputation Systems in Reducing On-Line Auction Fraud
International Journal of Electronic Commerce
A typology of complaints about eBay sellers
Communications of the ACM - The psychology of security: why do good users make bad decisions?
Toward a Comprehensive Model in Internet Auction Fraud Detection
HICSS '08 Proceedings of the Proceedings of the 41st Annual Hawaii International Conference on System Sciences
Crowdsourcing user studies with Mechanical Turk
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Incentivizing outsourced computation
Proceedings of the 3rd international workshop on Economics of networked systems
Reputation inflation detection in a Chinese C2C market
Electronic Commerce Research and Applications
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Fraud is a constant problem for online auction sites. Besides failures in detecting fraudsters, the currently employed methods yield many false positives: bona fide sellers that end up harassed by the auction site as suspects. We advocate the use of human computation (also called crowdsourcing) to improve precision and recall of current fraud detection techniques. To examine the feasibility of our proposal, we did a pilot study with a set of human subjects, testing whether they could distinguish fraudsters from common sellers before negative feedback arrived and looking just at a snapshot of seller profiles. Here we present the methodology used and the obtained results, in terms of precision and recall of human classifiers, showing positive evidence that detecting fraudsters with human computation is viable.