Abstract
Accurate and efficient rumor detection is critical for information governance, particularly in the context of the rapid spread of misinformation on social networks. Traditional rumor detection relied primarily on manual analysis. With the continuous advancement of technology, machine learning and deep learning approaches for rumor identification have gradually emerged and gained prominence. However, previous approaches often struggle to simultaneously capture both the sequential and the global structural relationships among topological nodes within a social network. To tackle this issue, we introduce a hybrid model for detecting rumors that integrates a Graph Convolutional Network (GCN) with a Transformer architecture, aiming to leverage the complementary strengths of structural and semantic feature extraction. Positional encoding helps preserve the sequential order of these nodes within the propagation structure. The use of Multi-head attention mechanisms enables the model to capture features across diverse representational subspaces, thereby enhancing both the richness and depth of text comprehension. This integration allows the framework to concurrently identify the key propagation network of rumors, the textual content, the long-range dependencies, and the sequence among propagation nodes. Experimental evaluations on publicly available datasets, including Twitter 15 and Twitter 16, demonstrate that our proposed fusion model significantly outperforms both standalone models and existing mainstream methods in terms of accuracy. These results validate the effectiveness and superiority of our approach for the rumor detection task.
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Data availability
The code and data employed in this study are available on the Open Science Framework: https://osf.io/stcp5/.
References
Bian T, Xiao X, Xu T, Zhao P, Huang W, Rong Y, Huang J (2020) Rumor detection on social media with bi-directional graph convolutional networks. In: Proceedings of the AAAI conference on artificial intelligence. Vol. 34, AAAI, pp 549–556
Castillo C, Mendoza M, Poblete B (2011) Information credibility on Twitter. In: Proceedings of the 20th international conference on World Wide Web. ACM, pp 675–684
Chen T, Wong RC-W (2020) Handling information loss of graph neural networks for session-based recommendation. In: Proceedings of the 26th ACM SIGKDD international conference on knowledge discovery & data mining. ACM, pp 1172–1180
Devlin J, Chang, M-W Lee K, Toutanova K (2019) Bert: pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the conference of the North American chapter of the Association for Computational Linguistics: Human Language Technologies. HLT-NAACL, pp 4171–4186
Dong M, Zheng B, Viet Hung NQ, Su H, Li G (2019) Multiple rumor source detection with graph convolutional networks. In: Proceedings of the 28th ACM international conference on information and knowledge management, ACM, pp 569–578
Dou Y, Shu K, Xia C, Yu PS, Sun L (2021) User preference-aware fake news detection. In: Proceedings of the 44th international ACM SIGIR conference on research and development in information retrieval. ACM, pp 2051–2055
Fang X, Yang H, Ding D, Gao W, Zhang L, Wang Y, Shi L (2024) Enhancing app usage prediction accuracy with GCN-transformer model and meta-path context. IEEE Access 12:53031–53044
Friggeri A, Adamic L, Eckles D, Cheng J (2014) Rumor cascades. In: Proceedings of the international AAAI conference on web and social media. Vol. 8. AAAI, pp 101–110
Huang X, Liu Y (2024) A bi-gru-dsa-based social network rumor detection approach. Open Comput Sci 14(1):20230114
Huang Q Yu J, Wu J, Wang B (2020) Heterogeneous graph attention networks for early detection of rumors on Twitter. In: Proceedings international joint conference on neural networks (IJCNN). IEEE, pp 1–8
Jin Z, Cao J, Guo H, Zhang Y, Luo J (2017) Multimodal fusion with recurrent neural networks for rumor detection on microblogs. In: Proceedings of the 25th ACM international conference on multimedia. ACM, pp 795–816
Jing Ma, WG, Wong K-F (2017) Detect rumors in microblog posts using propagation structure via kernel learning. In: Proceedings of the 55th annual meeting of the Association for Computational Linguistics. ACM, pp 708–717
Kwon S, Cha M, Jung K (2017) Rumor detection over varying time windows. PLoS ONE 12(1):e0168344
Kwon S, Cha M, Jung K, Chen W, Wang Y (2013) Prominent features of rumor propagation in online social media. In: Proceedings of the IEEE 13th international conference on data mining. IEEE, pp 1103–1108
Li Q, Han Z, Wu X-M (2018) Deeper insights into graph convolutional networks for semi-supervised learning. In: Proceedings of the AAAI conference on artificial intelligence. AAAI, pp 3538–3545
Liu X, Nourbakhsh A, Li Q, Fang R, Shah S (2015) Real-time rumor debunking on Twitter. In: Proceedings of the 24th ACM international conference on information and knowledge management, pp 1867–1870
Lu Y-J, Li C-Te (2020) Graph-aware co-attention networks for explainable fake news detection on social media. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. ACM, pp 505–514
Ma J, Gao W, Mitra P, Kwon S, Jansen BJ, Wong K-F, Cha M (2016) Detecting rumors from microblogs with recurrent neural networks. In: Proceedings of the twenty-fifth international joint conference on artificial intelligence. IJCAI-16, pp 3818–3824
Ma J, Gao W, Wei Z, Lu Y, Wong K-F (2015) Detect rumors using time series of social context information on microblogging websites. In: Proceedings of the 24th ACM international on conference on Information and Knowledge Management. ACM, p 1751–1754
Ma J, Gao, W, Wong K-F (2018) Rumor detection on Twitter with tree-structured recursive neural networks. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics. ACM, pp 1980–1989
Meel P, Kumar Vishwakarma D (2020) Fake news, rumor, information pollution in social media and web: a contemporary survey of state-of-the-art, challenges and opportunities. Expert Syst Appl 153:112986
Pattanaik B, Mandal S, Tripathy RM (2023) A survey on rumor detection and prevention in social media using deep learning. Knowl Inf Syst 65(10):3839–3880
Ramos J (2003) Using TF-IDF to determine word relevance in document queries. In: Proceedings of the first instructional conference on machine learning. Vol. 242 pp 29–48
Shah OI, Raza Rizvi D, Mir AN (2025) Transformer-based innovations in medical image segmentation: a mini review. SN Comput Sci 6(4):375
Sun Y, Sheng D, Zhou Z, Wu Y (2024) AI hallucination: towards a comprehensive classification of distorted information in artificial intelligence-generated content. Humanit Soc Sci Commun 11(1):1–14
Sun M, Zhang X, Zheng J, Ma G (2022) DDGCN: dual dynamic graph convolutional networks for rumor detection on social media. In: Proceedings of the AAAI conference on artificial intelligence. AAAI, pp 4611–4619
Tang H, Ji D, C Li C, Zhou Q (2020) Dependency graph enhanced dual-transformer structure for aspect-based sentiment classification. In: Proceedings 58th annual meeting of the association for computational linguistics. ACM, pp 6578–6588
Tao C, Gao S, Shang M, Wu W, Zhao D, Yan R (2018) Get the point of my utterance! learning towards effective responses with multi-head attention mechanism. IJCAI, pp 4418–4424
Thota NR, Sun X, Dai J (2023) Early rumor detection in social media based on graph convolutional networks. In: Proceedings international conference on computing, networking and communications (ICNC). IEEE, pp 516–522
Tu K, Chen C, Hou C, Yuan J, Li J, Yuan X (2021) Rumor2vec: a rumor detection framework with joint text and propagation structure representation learning. Inf Sci 560:137–151
Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser L, Polosukhin I (2017) Attention is all you need. In: Proceedings of the 31st conference on neural information processing systems (NIPS 2017). ACM, pp 1–11
Wang X, Li Y, Qiu C, Li J, Liu Y (2021) Research on reversal model and simulation of network rumor propagation under public health emergencies. Libr Inf Serv 65(19):4–15
Wu K, Yang S, Zhu KQ (2015) False rumors detection on Sina Weibo by propagation structures. In: Proceedings of the 31st international conference on data engineering, IEEE, pp 51–662
Xu K, Li C, Tian Y, T Sonobe T, Kawarabayashi K, Jegelka S (2018) Representation learning on graphs with jumping knowledge networks. In: Proceedings of the international conference on machine learning. PMLR, pp 5453–5462
Yang F, Liu Y, Yu X, Yang M (2012) Automatic detection of rumor on Sina Weibo. In: Proceedings ACM SIGKDD workshop on mining data semantics. ACM, pp 1–7
Ye N, Yu D, Zhou Y, Shang KK, Zhang S (2023) Graph convolutional-based deep residual modeling for rumor detection on social media. Mathematics 11(15):3393
Yu D, Zhou Y, Zhang S, Liu C (2022) Heterogeneous graph convolutional network-based dynamic rumor detection on social media. Complexity 2022(1):8393736
Yu F, Liu Q, Wu S, Wang L, Tan T et al. (2017) A convolutional approach for misinformation identification. IJCAI 2017:3901–3907
Zhang Y, Feng M, Shang K-ke, Ran Y, Wang C-J (2022) Peeking strategy for online news diffusion prediction via machine learning. Phys A Stat Mech Appl 598: 127357
Zhao Q, Zhang Y, Feng X (2024) Joint learning of structural and textual information on propagation network by graph attention networks for rumor detection. Appl Intell 54(3):2851–2866
Acknowledgements
This work was supported by National Social Science Funds of China (Grant No. 22BSH025), Zhejiang Provincial Natural Science Foundation of China (No. LTGG24F030002), National Natural Science Foundation of China (Grant No. 61803047), the Social Sciences Fund of Jiangsu Province (Grant No. 24XWB004), the Jiangsu Qing Lan Project, and the Special Research Project on the Digital Transformation of Higher Education and the Practice of Educational Modernization in Jiangsu Province (Grant No. 2024CXJG061).
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YY Writing, Coding, Investigation, Experimental analysis. SZ analyzed the data and revised the manuscript. DY Supervision, Conceptualization, Funding acquisition. YZ Data resources, analyzed the data. CW conceived the study, supervised the study. KS supervised the study and revised the manuscript.
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Yan, Y., Zhang, S., Yu, D. et al. TRGCN: a hybrid framework for social network rumor detection. Humanit Soc Sci Commun (2026). https://doi.org/10.1057/s41599-026-06946-1
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DOI: https://doi.org/10.1057/s41599-026-06946-1
