Spam Review Detection with Graph Convolutional Networks

Abstract

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.

Accepted at CIKM 2019

References in corpus (4)

• DeepWalk: Online Learning of Social Representations
• Graph Convolutional Neural Networks for Web-Scale Recommender Systems
• Attributed Social Network Embedding
• NetSpam: a Network-based Spam Detection Framework for Reviews in Online Social Media

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• Graph Mining for Cybersecurity: A Survey
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• HIN-RNN: A Graph Representation Learning Neural Network for Fraudster Group Detection With No Handcrafted Features