# Scraping and Preprocessing Commercial Auction Data for Fraud Classification

arXiv: https://arxiv.org/abs/1806.00656 · HTML: https://mlsanity.com/paper/1806.00656/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Jun 2018.

## Built on by (most cited first, 2 of 2)

- [Efficient Convex Algorithms for Universal Kernel Learning](https://mlsanity.com/paper/2304.07472/) (2023)
- [Knowledge Cascade: Reverse Knowledge Distillation on Nonparametric Multivariate Functional Estimation](https://mlsanity.com/paper/2606.25927/) (2026)

## Abstract (arXiv)

In the last three decades, we have seen a significant increase in trading goods and services through online auctions. However, this business created an attractive environment for malicious moneymakers who can commit different types of fraud activities, such as Shill Bidding (SB). The latter is predominant across many auctions but this type of fraud is difficult to detect due to its similarity to normal bidding behaviour. The unavailability of SB datasets makes the development of SB detection and classification models burdensome. Furthermore, to implement efficient SB detection models, we should produce SB data from actual auctions of commercial sites. In this study, we first scraped a large number of eBay auctions of a popular product. After preprocessing the raw auction data, we build a high-quality SB dataset based on the most reliable SB strategies. The aim of our research is to share the preprocessed auction dataset as well as the SB training (unlabelled) dataset, thereby researchers can apply various machine learning techniques by using authentic data of auctions and fraud.