Capturing Reviewer Behavioral Drift Over Time With Temporal Graph Networks for Adaptive Fake Review Detection
Keywords:
fake review detection, temporal graph networks, behavioral drift, dynamic graph learning, opinion spamAbstract
The proliferation of fake reviews on e-commerce and service platforms has severely undermined consumer trust and market integrity. Existing detection approaches predominantly rely on static graph structures or snapshot-based representations that fail to model the evolving behavioral patterns of individual reviewers over time. This paper proposes a Temporal Graph Network (TGN) based framework that captures reviewer behavioral drift through continuous-time dynamic graph modeling, enabling adaptive fake review detection. By encoding reviewer interaction histories, temporal embeddings, and relational evolution across heterogeneous review graphs, the proposed method learns time-aware representations that distinguish genuine reviewers from deceptive actors whose behavioral signatures shift in response to platform countermeasures. Experiments conducted on two benchmark datasets demonstrate that the proposed approach outperforms state-of-the-art baselines across precision, recall, and F1-score metrics. This work contributes a principled methodology for temporally adaptive fraud detection and highlights the utility of dynamic graph learning in combating evolving review manipulation strategies.
References
Hussain, N., Turab Mirza, H., Rasool, G., Hussain, I., & Kaleem, M. (2019). Spam review detection techniques: A systematic literature review. Applied Sciences, 9(5), 987.
Abdulqader, M., Namoun, A., & Alsaawy, Y. (2022). Fake online reviews: A unified detection model using deception theories. IEEE Access, 10, 128622-128655.
Ji, S. J., Zhang, Q., Li, J., Chiu, D. K., Xu, S., Yi, L., & Gong, M. (2020). A burst-based unsupervised method for detecting review spammer groups. Information Sciences, 536, 454-469.
Liu, Z., Dou, Y., Yu, P. S., Deng, Y., & Peng, H. (2020, July). Alleviating the inconsistency problem of applying graph neural network to fraud detection. In Proceedings of the 43rd international ACM SIGIR conference on research and development in information retrieval (pp. 1569-1572).
Dou, Y., Liu, Z., Sun, L., Deng, Y., Peng, H., & Yu, P. S. (2020, October). Enhancing graph neural network-based fraud detectors against camouflaged fraudsters. In Proceedings of the 29th ACM international conference on information & knowledge management (pp. 315-324).
Zhao, J., Shao, M., Tang, H., Liu, J., Du, L., & Wang, H. (2023). RHGNN: Fake reviewer detection based on reinforced heterogeneous graph neural networks. Knowledge-Based Systems, 280, 111029.
Garcia, C. M., Abilio, R., Koerich, A. L., Britto Jr, A. D. S., & Barddal, J. P. (2025). Concept drift adaptation in text stream mining settings: A systematic review. ACM Transactions on Intelligent Systems and Technology, 16(2), 1-67.
Suryawanshi, S., Goswami, A., & Patil, P. (2024). FakeIDCA: Fake news detection with incremental deep learning based concept drift adaption. Multimedia Tools and Applications, 83(10), 28579-28594.
Cai, L., Chen, Z., Luo, C., Gui, J., Ni, J., Li, D., & Chen, H. (2021, October). Structural temporal graph neural networks for anomaly detection in dynamic graphs. In Proceedings of the 30th ACM international conference on Information & Knowledge Management (pp. 3747-3756).
Rossi, E., Chamberlain, B., Frasca, F., Eynard, D., Monti, F., & Bronstein, M. (2020). Temporal graph networks for deep learning on dynamic graphs. arXiv preprint arXiv:2006.10637.
Feng, S., Tan, Z., Wan, H., Wang, N., Chen, Z., Zhang, B., ... & Luo, M. (2022). Twibot-22: Towards graph-based twitter bot detection. Advances in Neural Information Processing Systems, 35, 35254-35269.
Chen, J., Liu, J., Liang, Y., & Zhou, M. (2026). KE-MLLM: A Knowledge-Enhanced Multi-Sensor Learning Framework for Explainable Fake Review Detection. Applied Sciences, 16(6), 2909.
Li, A., Qin, Z., Liu, R., Yang, Y., & Li, D. (2019, November). Spam review detection with graph convolutional networks. In Proceedings of the 28th ACM international conference on information and knowledge management (pp. 2703-2711).
Kumar, N., Venugopal, D., Qiu, L., & Kumar, S. (2019). Detecting anomalous online reviewers: An unsupervised approach using mixture models. Journal of Management Information Systems, 36(4), 1313-1346.
Ren, Z., Zhang, X., Zhang, S., & Yang, C. (2023, September). Fake Review Detection via Heterogeneous Graph Attention Network. In International Conference on Artificial Neural Networks (pp. 364-376). Cham: Springer Nature Switzerland.
Guo, Y., Ling, Y., & Chen, H. (2020). A time-aware graph neural network for session-based recommendation. IEEE Access, 8, 167371-167382.
Xia, Z., Liu, C., Gong, N. Z., Li, Q., Cui, Y., & Song, D. (2019, July). Characterizing and detecting malicious accounts in privacy-centric mobile social networks: A case study. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (pp. 2012-2022).
Cheng, D., Wang, X., Zhang, Y., & Zhang, L. (2020). Graph neural network for fraud detection via spatial-temporal attention. IEEE Transactions on Knowledge and Data Engineering, 34(8), 3800-3813.
Wu, K., Lin, Y., Wang, X., & Wang, L. (2026). Multimodal Learning-Based Relational Graph Neural Networks for Social Bot Detection. IEEE Transactions on Network and Service Management.
Kim, Y., Lee, Y., Choe, M., Oh, S., & Lee, Y. (2024). Temporal graph networks for graph anomaly detection in financial networks. arXiv preprint arXiv:2404.00060.
Cong, W., Zhang, S., Kang, J., Yuan, B., Wu, H., Zhou, X., ... & Mahdavi, M. (2023). Do we really need complicated model architectures for temporal networks?. arXiv preprint arXiv:2302.11636.
Baek, J., Lee, D. B., & Hwang, S. J. (2020). Learning to extrapolate knowledge: Transductive few-shot out-of-graph link prediction. Advances in neural information processing systems, 33, 546-560.
Ding, J., Shen, Z., & Liu, W. (2026). Game-Theoretic Cost-Sensitive Adversarial Training for Robust Cloud Intrusion Detection Against GAN-Based Evasion Attacks. Applied Sciences, 16(8), 3944.
Ping, W., Jiao, Y., Fan, H., & Zhang, X. (2026). Multimodal Fraud Detection in Financial Statements: A Trimodal Attention Network with Contrastive Evidence Chain Construction. IEEE Access.
Wang, B., Wang, Z., Zhao, W., Zhang, F., & Shang, W. (2026). DRL-Adapt: Deep Reinforcement Learning for Adaptive Routing Convergence Optimization in Large-Scale Networks. IEEE Open Journal of the Computer Society.
Teng, D., Rhee, M., Qin, Y., Zi, B., & Liu, W. (2026). SW-SpeedDLM: Sliding-Window Speculative Decoding for Diffusion Language Models under Long-Context Constraints. Mathematics
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Olivia Martinez, Hannah Cooper, Hannah Cooper, Peter Collins
This work is licensed under a Creative Commons Attribution 4.0 International License.
All articles published in the Journal of Social Sciences, Humanities and Innovation (JSSHI) are licensed under the Creative Commons Attribution 4.0 International License (CC BY 4.0).
This license permits unrestricted use, distribution, reproduction, and adaptation in any medium or format, provided that the original author(s) and source are properly credited. Users are allowed to copy, redistribute, remix, transform, and build upon the material for any purpose, including commercial use, as long as appropriate attribution is given and any changes made are indicated.
Authors retain copyright of their work while granting the journal the right of first publication. The journal supports open access principles and encourages the wide dissemination of scholarly research to promote knowledge sharing and academic collaboration.
For more information about the license, visit: https://creativecommons.org/licenses/by/4.0/
