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

TL;DR

This paper introduces HIN-RNN, a neural network model that detects fraudster groups in social reviews without relying on handcrafted features, by learning representations from review texts and reviewer behaviors.

Contribution

HIN-RNN is the first neural approach for fraudster group detection that leverages heterogeneous information networks without handcrafted features.

Findings

Significant improvement over state-of-the-art on Yelp dataset

Enhanced recall and F1-score on Amazon dataset

Effective representation learning of reviewers and group behaviors

Abstract

Social reviews are indispensable resources for modern consumers' decision making. For financial gain, companies pay fraudsters preferably in groups to demote or promote products and services since consumers are more likely to be misled by a large number of similar reviews from groups. Recent approaches on fraudster group detection employed handcrafted features of group behaviors without considering the semantic relation between reviews from the reviewers in a group. In this paper, we propose the first neural approach, HIN-RNN, a Heterogeneous Information Network (HIN) Compatible RNN for fraudster group detection that requires no handcrafted features. HIN-RNN provides a unifying architecture for representation learning of each reviewer, with the initial vector as the sum of word embeddings of all review text written by the same reviewer, concatenated by the ratio of negative reviews. Given a co-review network representing reviewers who have reviewed the same items with the same ratings and the reviewers' vector representation, a collaboration matrix is acquired through HIN-RNN training. The proposed approach is confirmed to be effective with marked improvement over state-of-the-art approaches on both the Yelp (22% and 12% in terms of recall and F1-value, respectively) and Amazon (4% and 2% in terms of recall and F1-value, respectively) datasets.