Detecting problematic transactions in a consumer-to-consumer e-commerce network

Shun Kodate, Ryusuke Chiba, Shunya Kimura, Naoki Masuda*

*この研究の対応する著者

研究成果: Article査読

抄録

Providers of online marketplaces are constantly combatting against problematic transactions, such as selling illegal items and posting fictive items, exercised by some of their users. A typical approach to detect fraud activity has been to analyze registered user profiles, user’s behavior, and texts attached to individual transactions and the user. However, this traditional approach may be limited because malicious users can easily conceal their information. Given this background, network indices have been exploited for detecting frauds in various online transaction platforms. In the present study, we analyzed networks of users of an online consumer-to-consumer marketplace in which a seller and the corresponding buyer of a transaction are connected by a directed edge. We constructed egocentric networks of each of several hundreds of fraudulent users and those of a similar number of normal users. We calculated eight local network indices based on up to connectivity between the neighbors of the focal node. Based on the present descriptive analysis of these network indices, we fed twelve features that we constructed from the eight network indices to random forest classifiers with the aim of distinguishing between normal users and fraudulent users engaged in each one of the four types of problematic transactions. We found that the classifier accurately distinguished the fraudulent users from normal users and that the classification performance did not depend on the type of problematic transaction.

本文言語English
論文番号90
ジャーナルApplied Network Science
5
1
DOI
出版ステータスPublished - 2020 12
外部発表はい

ASJC Scopus subject areas

  • 一般
  • コンピュータ ネットワークおよび通信
  • 計算数学

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