PAGCN: Structural Semantic Relationship and Attention Mechanism for Rumor Detection
Source abstract
Abstract
Traditional GCN based methods capture the propagation structure between posts, but do not fully model dynamic semantic information, such as the role of specific users on the propagation path and the context of post content that changes over time, leading to a decrease in the accuracy of rumor detection. Therefore, we propose an innovative path attention graph convolution network (PAGCN) framework, which effectively solves this limitation by integrating propagation structure and semantic representation learning. PAGCN first uses the graph neural network (GNN) to model the information transmission path, focusing on the differences between rumor and fact information in communication behavior, such as the differences between depth first and breadth first dissemination modes. Then, in order to enhance the ability of semantic understanding, we design a multi head attention mechanism based on convolutional neural network (CNN), which extracts deep contextual relationships from text content. Furthermore, by introducing the comparative learning technology, PAGCN can adaptively optimize the representation of structural and semantic features, dynamically focus on the most discriminative features, and significantly improve the sensitivity to subtle patterns in rumor propagation. The experimental verification on three benchmark datasets of twitter15, twitter16, and Weibo, shows that the proposed PAGCN performs best among the 17 comparison models, and the accuracy rates on twitter15 and Weibo datasets are 90.9% and 93.9%, respectively, which confirms the effectiveness of the framework in capturing propagation structure and semantic information at the same time.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Misinformation and Its Impacts · Opinion Dynamics and Social Influence · Complex Network Analysis Techniques
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Computer Science and Engineering, Chongqing University of Technology, Chongqing 400054, China
Funding
Key Project of Chongqing Municipal Education Commission · Graduate Innovation Fund of Chongqing University of Technology
References (40)
Show source-provided references
1. Bian, T., Xiao, X., Xu, T.Y., Zhao, P.L., Huang, W.B., Rong, Y., and Huang, J.Z. (2020, January 7–12). Rumor detection on social media with bi-directional graph convolutional networks. Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence, Menlo Park, CA, USA.10.1609/aaai.v34i01.5393 ↗
2. GGDHSCL: A Graph Generative Diffusion with Hard Negative Sampling Contrastive Learning Recommendation Method
3. Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A., Kaiser, L., and Polosukhin, I. (2017, January 4–9). Attention Is All You Need. Proceedings of the Advances in Neural Information Processing Systems 30 (NIPS 2017), Long Beach, CA, USA.
4. SIFTER: A Framework for Robust Rumor Detection10.1109/TASLP.2022.3140474 ↗
5. GAWF: Influence Maximization Method Based on Graph Attention Weight Fusion10.1142/S0129183125420021 ↗
6. Dual channel semantic enhancement-based convolutional neural networks model for text classification10.1142/S0129183124420129 ↗
7. Yu, J.L., Yin, H.Z., Xia, X., Chen, T., Cui, L.Z., and Nguyen, Q.V.H. (2022, January 11–15). Are graph augmentations necessary? Simple graph contrastive learning for recommendation. Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, Madrid, Spain.10.1145/3477495.3531937 ↗
8. Devlin, J., Chang, M.-W., Lee, K., and Toutanova, K. (2019, January 2–7). BERT: Pre-training of deep bidirectional transformers for language understanding. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics, Minneapolis, MN, USA.
9. Ma, J., Gao, W., and Mitra, P. (2016, January 9–15). Detecting rumors from microblogs with recurrent neural networks. Proceedings of the 25th International Joint Conference on Artificial Intelligence, New York, NY, USA.
10. Social Network Rumor Detection Method Combining Dual-Attention Mechanism with Graph Convolutional Network10.1109/TCSS.2022.3184745 ↗
11. Bahdanau, D., Cho, K., and Bengio, Y. (2015, January 7–9). Neural Machine Translation by Jointly Learning to Align and Translate. Proceedings of the 3rd International Conference on Learning Representations (ICLR 2015), San Diego, CA, USA.
12. Veličković, P., Cucurull, G., Casanova, A., Romero, A., Liò, P., and Bengio, Y. (May, January 30). Graph attention networks. Proceedings of the International Conference on Learning Representations (ICLR), Vancouver, BC, Canada.
13. Defferrard, M., Bresson, X., and Vandergheynst, P. (2016, January 5–10). Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering. Proceedings of the 30th Conference on Neural Information Processing Systems (NeurIPS 2016), Barcelona, Spain.
14. Tschannen, M., Djolonga, J., Rubenstein, P.K., Gelly, S., and Lucic, M. (2019). On Mutual Information Maximization for Representation Learning. arXiv.
15. Ma, J., and Gao, W. (2020, January 8–13). Debunking Rumors on Twitter with Tree Transformer. Proceedings of the 28th International Conference on Computational Linguistics, Barcelona, Spain.10.18653/v1/2020.coling-main.476 ↗
16. Shu, K., Wang, S., and Liu, H. (2019, January 11–15). Beyond news contents: The role of social context for fake news detection. Proceedings of the 12th ACM International Conference on Web Search and Data Mining, Melbourne, Australia.10.1145/3289600.3290994 ↗
17. Kipf, T.N., and Welling, M. (2017, January 24–26). Semi-supervised classification with graph convolutional networks. Proceedings of the 2017 International Conference on Learning Representations, Toulon, France.
18. Mehta, N., Pacheco, M.L., and Goldisser, D. (2022, January 22–27). Tackling fake news detection by continually improving social context representations using graph neural networks. Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics, Dublin, Ireland.10.18653/v1/2022.acl-long.97 ↗
19. Gao, W., Fang, Y., Li, L., and Tao, X.H. (2021, January 26–29). Event detection in social media via graph neural network. Proceedings of the 22nd International Conference on Web Information Systems Engineering, Melbourne, Australia.10.1007/978-3-030-90888-1_28 ↗
20. Wei, L.W., Hu, D., Zhou, W., Yue, Z.J., and Hu, S.L. (2021, January 1–6). Edge-enhanced Bayesian graph convolutional networks for rumor detection. Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing, Bangkok, Thailand.10.18653/v1/2021.acl-long.297 ↗
21. Socher, R., Perelygin, A., Wu, J., Chuang, J., Manning, C., Ng, A., and Potts, C. (2013, January 18–21). Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank. Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing (EMNLP), Seattle, WA, USA.10.18653/v1/D13-1170 ↗
22. Detection of Rumors and Their Sources in Social Networks: A Comprehensive Survey10.1109/TBDATA.2024.3522801 ↗
23. DSMM: A dual stance-aware multi-task model for rumour veracity on social networks10.1016/j.ipm.2023.103528 ↗
24. Deep spatial-temporal structure learning for rumor detection10.1007/s00521-020-05236-4 ↗
25. An attention-based rumor detection model with tree-structured recursive neural networks10.1145/3391250 ↗
26. Khoo, L., Chieu, H., Qian, Z., and Jiang, J. (2020, January 7–12). Interpretable rumor detection in microblogs by attending to user interactions. Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence, Menlo Park, CA, USA.10.1609/aaai.v34i05.6405 ↗
27. Castillo, C., Mendoza, M., and Poblete, B. (April, January 28). Information credibility on Twitter. Proceedings of the 20th International Conference on World Wide Web, Hyderabad, India.
28. Ma, J., Gao, W., Wei, Z., Lu, Y., and Wong, K.F. (2015, January 19–23). Detect rumors using time series of social context information on microblogging websites. Proceedings of the 24th ACM International Conference on Information and Knowledge Management, Melbourne, Australia.10.1145/2806416.2806607 ↗
29. Lu, Y.-J., and Li, C.-T. (2020). GCAN: Graph-aware co-attention networks for explainable fake news detection on social media. arXiv.10.18653/v1/2020.acl-main.48 ↗
30. Wu, K., Yang, S., and Zhu, K. (2015, January 13–17). False rumors detection on Sina Weibo by propagation structures. Proceedings of the IEEE 31st International Conference on Data Engineering, Seoul, Republic of Korea.10.1109/ICDE.2015.7113322 ↗
31. Sun, T., Qian, Z., Dong, S., Li, P., and Zhu, Q. (2022, January 25–29). Rumor detection on social media with graph adversarial contrastive learning. Proceedings of the ACM Web Conference, Lyon, France.10.1145/3485447.3511999 ↗
32. Adversarial contrastive learning for evidence-aware fake news detection with graph neural networks
33. Self-supervised learning on graphs: Contrastive, generative, or predictive
34. Wu, S., Tang, Y., Zhu, Y., Wang, L., Xie, X., and Tan, T. (February, January 27). Session-based recommendation with graph neural networks. Proceedings of the AAAI Conference on Artificial Intelligence, Honolulu, HI, USA.
35. Xu, Y., Hu, J., Ge, J., Wu, Y., Li, T., and Li, H. (2023, January 4–10). Contrastive Learning at the Relation and Event Level for Rumor Detection. Proceedings of the ICASSP 2023 IEEE International Conference on Acoustics, Speech and Signal Processing, Rhodes Island, Greece.10.1109/ICASSP49357.2023.10096567 ↗
36. Lao, A., Zhang, Q., Shi, C., Cao, L., Yi, K., Hu, L., and Miao, D. (2024, January 20–27). Frequency Spectrum is More Effective for Multimodal Representation and Fusion: A Multimodal Spectrum Rumor Detector. Proceedings of the AAAI Conference on Artificial Intelligence, Vancouver, BC, Canada.10.1609/aaai.v38i16.29803 ↗
37. Silva, A., Luo, L., Karunasekera, S., and Leckie, C. (2023). Unsupervised Domain-agnostic Fake News Detection using Multi-modal Weak Signals. arXiv.10.1109/TKDE.2024.3392788 ↗
38. Li, L., Jin, D., Wang, X., Guo, F., Wang, L., and Dang, J. (2023, January 6–8). Multi-modal sarcasm detection based on cross-modal composition of inscribed entity relations. Proceedings of the 2023 IEEE 35th International Conference on Tools with Artificial Intelligence, Atlanta, GA, USA.10.1109/ICTAI59109.2023.00138 ↗
39. Zhou, Y., Yang, Y., Ying, Q., Qian, Z., and Zhang, X. (2023, January 10–14). Multimodal fake news detection via clip-guided learning. Proceedings of the IEEE International Conference on Multimedia and Expo, Brisbane, Australia.10.1109/ICME55011.2023.00480 ↗
40. Ying, Q., Hu, X., Zhou, Y., Qian, Z., Zeng, D., and Ge, S. (2023, January 7–14). Boot strapping multi-view representations for fake news detection. Proceedings of the AAAI Conference on Artificial Intelligence, Washington, DC, USA.10.1609/aaai.v37i4.25670 ↗