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Early rumor detection method based on stage sampling and triple-relationship graph

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Abstract

To address the problem that the influence of rumor propagation stages is ignored in traditional rumor detection methods, an early rumor detection method based on stage sampling and triple-relationship graph is proposed. Firstly, the sampling probability density function is defined, and the stage sampling method based on normal distribution is proposed to obtain the comment sets with respect to the early stage, middle stage, and late stage of an event. Secondly, both the comment posting time and information propagation directions are considered, and the triple-relationship graph that integrates diffusion relationship, aggregation relationship, and sibling relationship for each stage is constructed. Moreover, the advantage of heterogeneous graph attention network (HAN) in exploring graph structural features is leveraged to obtain the node representations. Finally, to improve the interpretability and to capture the mutual influence between nodes efficiently, a graph-level vector computation method based on compressed self-attention mechanism and soft attention mechanism is proposed. Experimental results on two public datasets show that the proposed method consistently outperforms existing typical methods, with Fw improvements of approximately 2.1% and 3.7% on the CED and Weibo datasets, respectively, validating its effectiveness on early rumor detection. Furthermore, attention weight visualization experiments explicitly highlight the contributions of comments at different stages, significantly enhancing the interpretability of our approach.

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No datasets were generated or analyzed during the current study.

References

  1. Askarizade M (2025) Enhancing rumor detection with data augmentation and generative pre-trained transformer. Expert Syst Appl 262:125649

    Article  MATH  Google Scholar 

  2. Bondielli A, Marcelloni F (2019) A survey on fake news and rumour detection techniques. Inf Sci 497:38–55

    Article  MATH  Google Scholar 

  3. Xu S, Liu X, Ma K et al (2023) Rumor detection on social media using hierarchically aggregated feature via graph neural networks. Appl Intell 53(3):3136–3149

    Article  MATH  Google Scholar 

  4. Maswadi K, Ghani NA, Hamid S et al (2021) Human activity classification using decision tree and Naive Bayes classifiers. Multimed Tools and Appl 80(14):21709–21726

    Article  Google Scholar 

  5. Cervantes J, Garcia-Lamont F, Rodríguez-Mazahua L et al (2020) A comprehensive survey on support vector machine classification: applications, challenges and trends. Neurocomputing 408:189–215

    Article  Google Scholar 

  6. Li Z, Liu F, Yang W et al (2021) A survey of convolutional neural networks: analysis, applications, and prospects. IEEE Trans Neural Networks And Learn Syst 33(12):6999–7019

    Article  MathSciNet  MATH  Google Scholar 

  7. Wang S, Feng Z (2024) Intelligent fault diagnosis of multi-sensor rolling bearings based on variational mode extraction and a lightweight deep neural network. Int J Signal and Imag Syst Eng 13(1):27–40

    MATH  Google Scholar 

  8. Oruh J, Viriri S, Adegun A (2022) Long short-term memory recurrent neural network for automatic speech recognition. IEEE Access 10:30069–30079

    Article  Google Scholar 

  9. Zhang Y, Yao S, Yang R et al (2022) Epileptic seizure detection based on bidirectional gated recurrent unit network. IEEE Trans Neural Syst Rehabil Eng 30:135–145

    Article  MATH  Google Scholar 

  10. Nikolentzos G, Tixier A, Vazirgiannis M (2020) Message passing attention networks for document understanding. In: Proceedings of the AAAI Conference on Artificial Intelligence vol 34, no 5, pp 8544–8551

  11. Bian T, Xiao X, Xu T et al (2020) Rumor detection on social media with bi-directional graph convolutional networks. arXiv

  12. Ni S, Li J, Kao HY (2022) HAT4RD: hierarchical adversarial training for rumor detection in social media. Sensors 22(17):6652

    Article  MATH  Google Scholar 

  13. Zheng J, Zhang X, Guo S et al (2022) MFAN: multi-modal feature-enhanced attention networks for rumor detection. IJCAI 2022:2413–2419

    MATH  Google Scholar 

  14. Parmar A, Katariya R, Patel V (2019) A review on random forest: an ensemble classifier. In: International Conference on Intelligent Data Communication Technologies and Internet of Things (ICICI) 2018. Springer International Publishing, pp 758-763

  15. Castillo C, Mendoza M, Poblete B (2011) Information credibility on twitter. In: Proceedings of the 20th International Conference on World Wide Web. ACM, Hyderabad India, pp 675–684

  16. Yang R, Zhang J, Gao X et al (2019) Simple and effective text matching with richer alignment features. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. Florence, Italy: Association for Computational Linguistics, p 4699–4709

  17. Della Vedova ML, Tacchini E et al (2018) Automatic online fake news detection combining content and social signals. In: 2018 22nd Conference of Open Innovations Association (FRUCT). IEEE, Jyvaskyla, pp 272-279

  18. Al-Ghadir AI, Azmi AM, Hussain A (2021) A novel approach to stance detection in social media tweets by fusing ranked lists and sentiments. Inform Fusion 67:29–40

    Article  MATH  Google Scholar 

  19. Douven I, Hegselmann R (2021) Mis-and disinformation in a bounded confidence model. Artif Intell 103415:291

    MathSciNet  MATH  Google Scholar 

  20. Fan TH, Wang IH (2018) Rumor source detection: a probabilistic perspective. In: IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp 4159–4163

  21. Esteban-Bravo M, Vidal-Sanz JM (2024) Predicting the virality of fake news at the early stage of dissemination. Expert Syst Appl 248:123390

    Article  MATH  Google Scholar 

  22. Kumar A, Bhatia MPS, Sangwan SR (2022) Rumour detection using deep learning and filter-wrapper feature selection in benchmark twitter dataset. Multimed Tools and Appl 81(24):34615–34632

    Article  MATH  Google Scholar 

  23. Yin S, Zhu P, Wu L et al (2024) GAMC: An unsupervised method for fake news detection using graph autoencoder with masking. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol 38, no 1, pp 347–355

  24. Ma J, Gao W, Mitra P et al (2016) Detecting rumors from microblogs with recurrent neural networks. In: International Joint Conference on Artificial Intelligence. IJCAI, New York

  25. Yu F, Liu Q, Wu S et al (2017) A convolutional approach for misinformation identification. In: Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence. Melbourne, Australia: International Joint Conferences on Artificial Intelligence Organization, 3901–3907[2022–11–08]

  26. Ma J, Gao W, Wong KF. (2018) Rumor detection on twitter with tree-structured recursive neural networks. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics. Association for Computational Linguistics, Melbourne, Australia, pp 1980–1989

  27. Kumar S, Carley K. (2019) Tree LSTMs with convolution units to predict stance and rumor veracity in social media conversations. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. Association for Computational Linguistics, Florence, Italy, pp 047–5058

  28. Lao A, Shi C, Yang Y (2021) Rumor detection with field of linear and non-linear propagation. In: Proceedings of the Web Conference 2021, pp 3178–3187

  29. Vosoughi S, Roy D, Aral S (2018) The spread of true and false news online. Science 359(6380):1146–1151

    Article  MATH  Google Scholar 

  30. El Alaoui D, Riffi J, Sabri A et al (2022) Deep GraphSAGE-based recommendation system: jumping knowledge connections with ordinal aggregation network. Neural Comput Appl 34(14):11679–11690

    Article  MATH  Google Scholar 

  31. El Alaoui D, Riffi J, Sabri A et al (2024) Contextual recommendations: dynamic graph attention networks with edge adaptation. IEEE Access 12:151019–151029

    Article  MATH  Google Scholar 

  32. Liu T, Wang Y, Ying R et al (2024) MuSe-GNN: learning unified gene representation from multimodal biological graph data. Adv Neural Inform Process Syst 2024:36

    MATH  Google Scholar 

  33. Gligorijević V, Renfrew PD, Kosciolek T et al (2021) Structure-based protein function prediction using graph convolutional networks. Nat Commun 12(1):3168

    Article  Google Scholar 

  34. Huang Q, Yu J, Wu J et al (2020) Heterogeneous graph attention networks for early detection of rumors on twitter. In: 2020 International Joint Conference on Neural Networks (IJCNN). IEEE, pp 1–8

  35. Yang YJ, Wang L, Wang YH (2021) Rumor detection based on source information and gating graph neural network. J Comput Res Develop 58(7):1412–1424

    MATH  Google Scholar 

  36. He Z, Li C, Zhou F et al (2021) Rumor detection on social media with event augmentations. In: Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM, Virtual Event Canada, pp 2020–2024

  37. Lin H, Ma J, Chen L et al (2022) Detect rumors in microblog posts for low-resource domains via adversarial contrastive learning. arXiv preprint arXiv:2204.08143

  38. Wei L, Hu D, Zhou W et al (2022) Uncertainty-aware Propagation Structure Reconstruction for Fake News Detection. In: Proceedings of the 29th International Conference on Computational Linguistics, pp 2759–2768

  39. Sun M, Zhang X, Zheng J et al (2022) Ddgcn: Dual dynamic graph convolutional networks for rumor detection on social media. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol 36, no 4, pp 4611–4619

  40. Khoo L M S, Chieu H L, Qian Z et al (2020) Interpretable rumor detection in microblogs by attending to user interactions. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol 34, no 05, pp 8783–8790

  41. Tian L, Zhang XJ, Lau JH (2022) Duck: Rumour detection on social media by modelling user and comment propagation networks. In: Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp 4939-4949

  42. Sun L, Rao Y, Lan Y et al (2023) Hg-sl: Jointly learning of global and local user spreading behavior for fake news early detection. In: Proceedings of the AAAI Conference on Artificial Intelligence vol 37, no 4, pp 5248–5256

  43. Chang Q, Li X, Duan Z (2024) A novel approach for rumor detection in social platforms: memory-augmented transformer with graph convolutional networks. Knowl-Based Syst 292:111625

    Article  MATH  Google Scholar 

  44. Chen J, Zhang W, Ma H et al (2023) Rumor detection in social media based on multi-hop graphs and differential time series. Mathematics 11(16):3461

    Article  MATH  Google Scholar 

  45. Yang R, Ma J, Lin H, et al. (2022) A weakly supervised propagation model for rumor verification and stance detection with multiple instance learning. In: Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp 1761–1772

  46. Yang R, Gao W, Ma J, et al. (2023) WSDMS: debunk fake news via weakly supervised detection of misinforming sentences with contextualized social wisdom. arXiv preprint arXiv:2310.16579

  47. Manurung J, Sihombing P, Budiman M A. (2023) Dynamic rumor control in social networks using temporal graph neural networks. In: 2023 International Conference of Computer Science and Information Technology (ICOSNIKOM). IEEE, pp 1–5

  48. Wu Z, Pi D, Chen J et al (2020) Rumor detection based on propagation graph neural network with attention mechanism. Expert Syst Appl 158:113595

    Article  MATH  Google Scholar 

  49. Qian W, Chai J, Xu Z et al (2018) Differential evolution algorithm with multiple mutation strategies based on roulette wheel selection. Appl Intell 48:3612–3629

    Article  MATH  Google Scholar 

  50. Kingma D P, Ba J (2017) Adam: a method for stochastic optimization. arXiv

  51. Song C, Yang C, Chen H et al (2019) CED: credible early detection of social media rumors. IEEE Trans Knowl Data Eng 33(8):3035–3047

    Article  MATH  Google Scholar 

  52. Hinojosa Lee MC, Braet J, Springael J (2024) Performance metrics for multilabel emotion classification: comparing micro, macro, and weighted f1-scores. Appl Sci 14(21):9863

    Article  MATH  Google Scholar 

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Acknowledgements

This research is supported by the National Natural Science Foundation of China (No. 61906220), the Ministry of education of Humanities and Social Science project (No. 19YJCZH178), and the Emerging Interdisciplinary Project of CUFE.

Funding

National Natural Science Foundation of China, 61906220, 61906220, and 61906220.

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Authors and Affiliations

  1. School of Information, Central University of Finance and Economics, Beijing, 100081, China

    Youwei Wang & Yan Zhang

  2. School of Statistics, Tianjin University of Finance and Economics, Tianjin, 300222, China

    Lizhou Feng

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  1. Youwei Wang
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  2. Lizhou Feng
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  3. Yan Zhang
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Contributions

Youwe Wang wrote the main manuscript text; Lizhou Feng reviewed the manuscript; and Yan Zhang prepared the figures and tables.

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Correspondence to Youwei Wang.

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Wang, Y., Feng, L. & Zhang, Y. Early rumor detection method based on stage sampling and triple-relationship graph. J Supercomput 81, 476 (2025). https://doi.org/10.1007/s11227-025-06959-8

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