Graph-aware deep fusion networks for online spam review detection

Li HE, Guandong XU, Shoaib JAMEEL, Xianzhi WANG, Hongxu CHEN

Research output: Contribution to journalArticlespeer-review

2 Citations (Scopus)

Abstract

Product reviews on e-commerce platforms play a critical role in shaping users' purchasing decisions. Unfortunately, online reviews sometimes can be intentionally misleading to manipulate the ecosystem. To date, existing methods to automatically detect 'spam reviews' either focus on sophisticated feature engineering with traditional classification models or rely on tuning neural networks with aggregated features. In this article, we develop a novel graph-based model, namely, graph-aware deep fusion networks (GDFNs) that use information from relevant metadata (review text, features of users, and items) and relational data (network) to capture the semantic information from their complex heterogeneous interactions via graph convolutional networks (GCNs). Besides, GDFN also uses a novel fusion technique to synthesize low- and high-order interactions with propagated information across multiple review-related subgraphs. Extensive experiments on publicly available datasets show that our proposed model is effective and outperforms several strong state-of-the-art baselines. Copyright © 2022 IEEE.

Original languageEnglish
Pages (from-to)2557-2565
JournalIEEE Transactions on Computational Social Systems
Volume10
Issue number5
Early online dateJul 2022
DOIs
Publication statusPublished - Oct 2023

Citation

He, L., Xu, G., Jameel, S., Wang, X., & Chen, H. (2023). Graph-aware deep fusion networks for online spam review detection. IEEE Transactions on Computational Social Systems, 10(5), 2557-2565. https://doi.org/10.1109/TCSS.2022.3189813

Keywords

  • E-commerce
  • Graph convolutional networks (GCNs),
  • Online review
  • Spam detection

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