  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.
