Angels or Demons? Classifying Desirable Heavy Users and Undesirable Power Sellers in Online C2C Marketplace

To grow and succeed, online consumer-to-consumer (C2C) marketplaces need to increase the number of users and transactions because their main revenue is usually the transaction fee. To increase the number of users and transactions, uncertainty must be reduced and a safe and enjoyable transaction environment must be maintained. In this paper, we aim to detect malicious users and power sellers who can harm the healthy growth of an online C2C platform. Using the dataset of a major online C2C marketplace, we classified undesirable users by building a classification model for banned users. The results of the banned user prediction indicated that most banned users are heavy sellers. Heavy sellers are desirable from the viewpoint of increasing the transaction fee revenue, but many are power sellers who are running full-time businesses on the platform, making it difficult for non-professional sellers to compete, and their dominance may eventually alienate users. Thus, we built another classification model to classify desirable and undesirable power sellers. Applying the model to the CART classifier, we successfully classified non-professional heavy users and undesirable power sellers in an online C2C marketplace