  Customers make a lot of reviews on online shopping websites every day, e.g.,
Amazon and Taobao. Reviews affect the buying decisions of customers, meanwhile,
attract lots of spammers aiming at misleading buyers. Xianyu, the largest
second-hand goods app in China, suffering from spam reviews. The anti-spam
system of Xianyu faces two major challenges: scalability of the data and
adversarial actions taken by spammers. In this paper, we present our technical
solutions to address these challenges. We propose a large-scale anti-spam
method based on graph convolutional networks (GCN) for detecting spam
advertisements at Xianyu, named GCN-based Anti-Spam (GAS) model. In this model,
a heterogeneous graph and a homogeneous graph are integrated to capture the
local context and global context of a comment. Offline experiments show that
the proposed method is superior to our baseline model in which the information
of reviews, features of users and items being reviewed are utilized.
Furthermore, we deploy our system to process million-scale data daily at
Xianyu. The online performance also demonstrates the effectiveness of the
proposed method.
