Disinformation propagation modeling in digital information warfare using hybrid GNN and LSTM
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Abstract
The rapid growth of digital information warfare has enabled the widespread dissemination of disinformation, posing serious challenges for detection systems. However, most existing approaches treat disinformation detection as a static classification problem and fail to consider the network structure and temporal dynamics of information spread. This study proposes a hybrid deep learning model that combines Graph Attention Networks (GAT) and Bidirectional Long Short-Term Memory (BiLSTM) with a cross-attention mechanism to capture both structural and temporal patterns of disinformation propagation. The proposed model was evaluated using three datasets: the PHEME rumor dataset, a large-scale Twitter and X crisis dataset, and a synthetically generated defense simulation dataset. Experimental results show that the model achieves strong performance, with 92.47% accuracy in classification, 89.63% precision in cascade prediction, 87.91% F1-score in source identification, and a mean absolute error of 0.183 in predicting spread dynamics, outperforming several baseline methods. These findings demonstrate that integrating network-based and temporal modeling can significantly improve disinformation detection performance. Future research will focus on incorporating multimodal data, real-time processing, and cross-platform learning to enhance the robustness of the proposed approach.
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Bai, L., Jia, C., Song, Z., & Cui, C. (2024). {VGA}: Vision and Graph Fused Attention Network for Rumor Detection. ACM Transactions on Information Systems. https://doi.org/10.1145/3722225
Dhawan, M., Sharma, S., Kadam, A., Sharma, R., & Kumaraguru, P. (2022). {GAME-ON}: Graph Attention Network Based Multimodal Fusion for Fake News Detection. Social Network Analysis and Mining. https://doi.org/10.48550/arXiv.2202.12478
Go, J. H., Sari, A., Jiang, J., Yang, S., & Jha, S. (2022). Fake News Quick Detection on Dynamic Heterogeneous Information Networks. ArXiv Preprint. https://doi.org/10.48550/arXiv.2205.07039
Gong, S., Sinnott, R. O., Qi, J., & Paris, C. (2023). Fake News Detection Through Graph-based Neural Networks: A Survey. ArXiv Preprint. https://doi.org/10.48550/arXiv.2307.12639
Han, Y., Silva, A., Luo, L., Karunasekera, S., & Leckie, C. (2021). Knowledge Enhanced Multi-Modal Fake News Detection. ArXiv Preprint. https://doi.org/10.48550/arXiv.2108.04418
Jeong, U., Ding, K., Cheng, L., Guo, R., Shu, K., & Liu, H. (2022). Nothing Stands Alone: Relational Fake News Detection with Hypergraph Neural Networks. Proceedings of the 2022 IEEE International Conference on Big Data. https://doi.org/10.48550/arXiv.2212.12621
Kananian, M., Badiei, F., & Ghahramani, S. A. G. (2023). {GRaMuFeN}: Graph-Based Multi-Modal Fake News Detection in Social Media. ArXiv Preprint. https://doi.org/10.48550/arXiv.2310.07668
Lin, H., Ma, J., Chen, L., Yang, Z., Cheng, M., & Chen, G. (2022). Detect Rumors in Microblog Posts for Low-Resource Domains via Adversarial Contrastive Learning. Proceedings of the 2022 Annual Conference of the North American Chapter of the Association for Computational Linguistics. https://doi.org/10.48550/arXiv.2204.08143
Lin, H., Ma, J., Cheng, M., Yang, Z., Chen, L., & Chen, G. (2021). Rumor Detection on Twitter with Claim-Guided Hierarchical Graph Attention Networks. Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing. https://doi.org/10.48550/arXiv.2110.04522
Lin, H., Ma, J., Yang, R., Yang, Z., & Cheng, M. (2023). A Unified Contrastive Transfer Framework with Propagation Structure for Boosting Low-Resource Rumor Detection. ArXiv Preprint. https://doi.org/10.48550/arXiv.2304.01492
Liu, J., Xie, J., Zhang, F., Zhang, Q., & Zha, Z. (2023). Knowledge-Enhanced Hierarchical Information Correlation Learning for Multi-Modal Rumor Detection. ArXiv Preprint. https://doi.org/10.48550/arXiv.2306.15946
Meel, P., & Vishwakarma, D. K. (2021). Fake News Detection Using Semi-Supervised Graph Convolutional Network. ArXiv Preprint. https://doi.org/10.48550/arXiv.2109.13476
Panayotov, P., Shukla, U., Sencar, H. T., Nabeel, M., & Nakov, P. (2022). {GREENER}: Graph Neural Networks for News Media Profiling. ArXiv Preprint. https://doi.org/10.48550/arXiv.2211.05533
Pelrine, K., Danovitch, J., & Rabbany, R. (2021). The Surprising Performance of Simple Baselines for Misinformation Detection. ArXiv Preprint. https://doi.org/10.48550/arXiv.2104.06952
Ren, Y., Wang, B., Zhang, J., & Chang, Y. (2021). Adversarial Active Learning Based Heterogeneous Graph Neural Network for Fake News Detection. Proceedings of the 2021 IEEE International Conference on Data Mining. https://doi.org/10.48550/arXiv.2101.11206
Saikia, P., Gundale, K., Jain, A., Jadeja, D., Patel, H., & Roy, M. (2022). Modelling Social Context for Fake News Detection: A Graph Neural Network Based Approach. Proceedings of the 2022 International Joint Conference on Neural Networks. https://doi.org/10.48550/arXiv.2207.13500
Trstanova, Z., El Manouzi, N., Chen, M., da Cunha, A. L. V, & Ivanov, S. (2022). Multilingual Disinformation Detection for Digital Advertising. Disinformation Countermeasures and Machine Learning Workshop at ICML 2022. https://doi.org/10.48550/arXiv.2207.10649
Wang, H., Bai, C., & Yao, J. (2022). Federated Graph Attention Network for Rumor Detection. ArXiv Preprint. https://doi.org/10.48550/arXiv.2206.05713
Wang, H., Dou, Y., Chen, C., Sun, L., Yu, P. S., & Shu, K. (2023). Attacking Fake News Detectors via Manipulating News Social Engagement. Proceedings of the ACM Web Conference 2023. https://doi.org/10.48550/arXiv.2302.07363
Wu, J., & Hooi, B. (2023). {DECOR}: Degree-Corrected Social Graph Refinement for Fake News Detection. Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. https://doi.org/10.1145/3580305.3599298
Wu, J., Xu, W., Liu, Q., Wu, S., & Wang, L. (2022). Adversarial Contrastive Learning for Evidence-Aware Fake News Detection with Graph Neural Networks. ArXiv Preprint. https://doi.org/10.48550/arXiv.2210.05498
Xu, W., Wu, J., Liu, Q., Wu, S., & Wang, L. (2022). Evidence-Aware Fake News Detection with Graph Neural Networks. Proceedings of the ACM Web Conference 2022. https://doi.org/10.48550/arXiv.2201.06885
Zhang, K., Yu, J., Shi, H., Liang, J., & Zhang, X.-Y. (2023). Rumor Detection with Diverse Counterfactual Evidence. ArXiv Preprint. https://doi.org/10.48550/arXiv.2307.09296
Zhang, X., & Gao, W. (2024). Predicting Viral Rumors and Vulnerable Users for Infodemic Surveillance. Information Processing and Management. https://doi.org/10.48550/arXiv.2401.09724
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