Detecting Fraudsters in Online Auction Using Variations of Neighbor Diversity

Inflated reputation fraud is a serious problem in online auction. Recent work suggested that neighbor diversity is an effective feature for discerning fraudsters from normal users. However, there exist many different methods to quantify diversity in the literature. This raises the problem of finding the most suitable method to calculate neighbor diversity for detecting fraudsters. We collect four different methods to quantify diversity, and apply them to calculate neighbor diversity. We then use these various neighbor diversities for fraudster detection. Experimental results on a real-world dataset demonstrate that, although these diversities were calculated differently, their performances on fraudster detection are similar. This finding reflects the robustness of neighbor diversity, regardless of how the diversity is calculated

Using Neighbor Diversity to Detect Fraudsters in Online Auctions

Online auctions attract not only legitimate businesses trying to sell their products but also fraudsters wishing to commit fraudulent transactions. Consequently, fraudster detection is crucial to ensure the continued success of online auctions. This paper proposes an approach to detect fraudsters based on the concept of neighbor diversity. The neighbor diversity of an auction account quantifies the diversity of all traders that have transactions with this account. Based on four different features of each trader (i.e., the number of received ratings, the number of cancelled transactions, k-core, and the joined date), four measurements of neighbor diversity are proposed to discern fraudsters from legitimate traders. An experiment is conducted using data gathered from a real world auction website. The results show that, although the use of neighbor diversity on k-core or on the joined date shows little or no improvement in detecting fraudsters, both the neighbor diversity on the number of received ratings and the neighbor diversity on the number of cancelled transactions improve classification accuracy, compared to the state-of-the-art methods that use k-core and center weight

Using Neighbor Diversity to Detect Fraudsters in Online Auctions

Online auctions attract not only legitimate businesses trying to sell their products but also fraudsters wishing to commit fraudulent transactions. Consequently, fraudster detection is crucial to ensure the continued success of online auctions. This paper proposes an approach to detect fraudsters based on the concept of neighbor diversity. The neighbor diversity of an auction account quantifies the diversity of all traders that have transactions with this account. Based on four different features of each trader (i.e., the number of received ratings, the number of cancelled transactions, k-core, and the joined date), four measurements of neighbor diversity are proposed to discern fraudsters from legitimate traders. An experiment is conducted using data gathered from a real world auction website. The results show that, although the use of neighbor diversity on k-core or on the joined date shows little or no improvement in detecting fraudsters, both the neighbor diversity on the number of received ratings and the neighbor diversity on the number of cancelled transactions improve classification accuracy, compared to the state-of-the-art methods that use k-core and center weight