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Related Experiment Video

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Spatio-Temporal Graph Representation Learning for Fraudster Group Detection.

Saeedreza Shehnepoor, Roberto Togneri, Wei Liu

IEEE Transactions on Neural Networks and Learning Systems

|October 24, 2022

Summary

This summary is machine-generated.

This study introduces FGDT, a novel framework for detecting fake review groups by analyzing temporal reviewer relationships. FGDT effectively identifies and removes outlier reviewers, improving fraud detection accuracy.

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Area of Science:

Computer Science
Data Mining
Network Analysis

Background:

Fake reviews are a significant problem, misleading consumers and distorting online marketplaces.
Existing fraud detection methods often overlook the temporal dynamics of reviewer behavior, limiting their effectiveness in identifying sophisticated fraud groups.

Purpose of the Study:

To propose a new framework, FGDT (Fraudster Group Detection through Temporal Relations), to accurately detect groups of fraudsters posting fake reviews.
To address the limitations of static network analysis by incorporating longitudinal reviewer behavior and coreview dynamics.

Main Methods:

FGDT utilizes a Heterogeneous Information Network-Recurrent Neural Network (HIN-RNN) to learn reviewer representations and capture collaboration patterns within a 28-day window.
A secondary RNN predicts spatio-temporal reviewer relations, which are then used by a Graph Convolutional Network (GCN) to refine reviewer representations and identify outliers.
The refined representations are averaged and fed into a fully connected layer for fraud group classification.

Main Results:

FGDT demonstrated significant improvements over recent approaches, achieving a 5% (4%), 12% (5%), and 12% (5%) increase in precision, recall, and F1-score, respectively, on the Yelp (Amazon) datasets.
The framework successfully identified and excluded outlier reviewers, enhancing the accuracy of fraud group detection.

Conclusions:

FGDT effectively detects fake review groups by leveraging temporal reviewer relationships and advanced deep learning techniques.
The proposed method offers a more robust and generalizable approach to online review fraud detection compared to existing static network analysis models.