Skip to main content

Advertisement

Springer Nature Link
Log in

Rumor detection with self-supervised learning on texts and social graph

  • Research Article
  • Published:
Frontiers of Computer Science Aims and scope Submit manuscript

Abstract

Rumor detection has become an emerging and active research field in recent years. At the core is to model the rumor characteristics inherent in rich information, such as propagation patterns in social network and semantic patterns in post content, and differentiate them from the truth. However, existing works on rumor detection fall short in modeling heterogeneous information, either using one single information source only (e.g., social network, or post content) or ignoring the relations among multiple sources (e.g., fusing social and content features via simple concatenation).

Therefore, they possibly have drawbacks in comprehensively understanding the rumors, and detecting them accurately. In this work, we explore contrastive self-supervised learning on heterogeneous information sources, so as to reveal their relations and characterize rumors better. Technically, we supplement the main supervised task of detection with an auxiliary self-supervised task, which enriches post representations via post self-discrimination.

Specifically, given two heterogeneous views of a post (i.e., representations encoding social patterns and semantic patterns), the discrimination is done by maximizing the mutual information between different views of the same post compared to that of other posts. We devise cluster-wise and instance-wise approaches to generate the views and conduct the discrimination, considering different relations of information sources. We term this framework as self-supervised rumor detection (SRD). Extensive experiments on three real-world datasets validate the effectiveness of SRD for automatic rumor detection on social media.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

Explore related subjects

Discover the latest articles and news from researchers in related subjects, suggested using machine learning.

References

  1. Farajtabar M, Yang J, Ye X, Xu H, Trivedi R, Khalil E, Li S, Song L, Zha H. Fake news mitigation via point process based intervention. In: Proceedings of the 34th International Conference on Machine Learning. 2017, 1097–1106

  2. Jin Z, Cao J, Guo H, Zhang Y, Wang Y, Luo J. Detection and analysis of 2016 US presidential election related rumors on twitter. In: Proceedings of the 10th International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation. 2017, 14–24

  3. Ruchansky N, Seo S, Liu Y. CSI: a hybrid deep model for fake news detection. In: Proceedings of 2017 ACM on Conference on Information and Knowledge Management. 2017, 797–806

  4. Shu K, Cui L, Wang S, Lee D, Liu H. dEFEND: explainable fake news detection. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 2019, 395–405

  5. Bian T, Xiao X, Xu T, Zhao P, Huang W, Rong Y, Huang J. Rumor detection on social media with bi-directional graph convolutional networks. In: Proceedings of the 34th AAAI Conference on Artificial Intelligence. 2020, 549–556

  6. Wu Z, Pi D, Chen J, Xie M, Cao J. Rumor detection based on propagation graph neural network with attention mechanism. Expert Systems with Applications, 2020, 158: 113595

    Article  Google Scholar 

  7. Ma J, Gao W, Wong K F. Rumor detection on twitter with tree-structured recursive neural networks. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics. 2018, 1980–1989

  8. Yang X, Lyu Y, Tian T, Liu Y, Liu Y, Zhang X. Rumor detection on social media with graph structured adversarial learning. In: Proceedings of the 29th International Joint Conference on Artificial Intelligence. 2020, 1417–1423

  9. Nguyen V H, Sugiyama K, Nakov P, Kan M Y. FANG: leveraging social context for fake news detection using graph representation. In: Proceedings of the 29th ACM International Conference on Information & Knowledge Management. 2020, 1165–1174

  10. Foroozani A, Ebrahimi M. Anomalous information diffusion in social networks: Twitter and Digg. Expert Systems with Applications, 2019, 134: 249–266

    Article  Google Scholar 

  11. Zubiaga A, Liakata M, Procter R, Hoi G W S, Tolmie P. Analysing how people orient to and spread rumours in social media by looking at conversational threads. PLoS One, 2016, 11(3): e0150989

    Article  Google Scholar 

  12. Chen T, Kornblith S, Norouzi M, Hinton G. A simple framework for contrastive learning of visual representations. In: Proceedings of the 37th International Conference on Machine Learning. 2020, 149

  13. Devlin J, Chang M W, Lee K, Toutanova K. BERT: pre-training of deep bidirectional transformers for language understanding. In: Proceedings of 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 2019, 4171–4186

  14. He K, Fan H, Wu Y, Xie S, Girshick R. Momentum contrast for unsupervised visual representation learning. In: Proceedings of 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2020, 9726–9735

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

  16. Yang F, Liu Y, Yu X, Yang M. Automatic detection of rumor on Sina Weibo. In: Proceedings of the ACM SIGKDD Workshop on Mining Data Semantics. 2012, 13

  17. Ma J, Gao W, Wei Z, Lu Y, Wong K F. 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. 2015, 1751–1754

  18. Liu X, Nourbakhsh A, Li Q, Fang R, Shah S. Real-time rumor debunking on twitter. In: Proceedings of the 24th ACM International on Conference on Information and Knowledge Management. 2015, 1867–1870

  19. Ma J, Gao W, Wong K F. Detect rumors in microblog posts using propagation structure via kernel learning. In: Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics. 2017, 708–717

  20. Ma J, Gao W, Mitra P, Kwon S, Jansen B J, Wong K F, Cha M. Detecting rumors from microblogs with recurrent neural networks. In: Proceedings of the 25th International Joint Conference on Artificial Intelligence. 2016, 3818–3824

  21. Jin Z, Cao J, Guo H, Zhang Y, Luo J. Multimodal fusion with recurrent neural networks for rumor detection on microblogs. In: Proceedings of the 25th ACM international conference on Multimedia. 2017, 795–816

  22. Chen T, Li X, Yin H, Zhang J. Call attention to rumors: deep attention based recurrent neural networks for early rumor detection. In: Proceedings of the Pacific-Asia Conference on Knowledge Discovery and Data Mining. 2018, 40–52

  23. Guo H, Cao J, Zhang Y, Guo J, Li J. Rumor detection with hierarchical social attention network. In: Proceedings of the 27th ACM International Conference on Information and Knowledge Management. 2018, 943–951

  24. Li Q, Zhang Q, Si L. Rumor detection by exploiting user credibility information, attention and multi-task learning. In: Proceedings of the 57th Conference of the Association for Computational Linguistics. 2019, 1173–1179

  25. Yu F, Liu Q, Wu S, Wang L, Tan T. A convolutional approach for misinformation identification. In: Proceedings of the 26th International Joint Conference on Artificial Intelligence. 2017, 3901–3907

  26. Kumar S, Asthana R, Upadhyay S, Upreti N, Akbar M. Fake news detection using deep learning models: a novel approach. Transactions on Emerging Telecommunications Technologies, 2020, 31(2): e3767

    Article  Google Scholar 

  27. Liu Y, Wu Y F B. Early detection of fake news on social media through propagation path classification with recurrent and convolutional networks. In: Proceedings of the 32nd AAAI Conference on Artificial Intelligence. 2018, 354–361

  28. Rao D, Miao X, Jiang Z, Li R. STANKER: stacking network based on level-grained attention-masked BERT for rumor detection on social media. In: Proceedings of 2021 Conference on Empirical Methods in Natural Language Processing. 2021, 3347–3363

  29. Song Y Z, Chen Y S, Chang Y T, Weng S Y, Shuai H H. Adversary-aware rumor detection. In: Proceedings of the 59th Findings of the Association for Computational Linguistics. 2021, 1371–1382

  30. Li J, Ni S, Kao H Y. Meet the truth: leverage objective facts and subjective views for interpretable rumor detection. In: Proceedings of the 59th Findings of the Association for Computational Linguistics. 2021, 705–715

  31. Kipf T N, Welling M. Semi-supervised classification with graph convolutional networks. In: Proceedings of the 5th International Conference on Learning Representations. 2017

  32. Zhang M, Chen Y. Link prediction based on graph neural networks. In: Proceedings of the 32nd International Conference on Neural Information Processing Systems. 2018, 5171–5181

  33. Pan S, Wu J, Zhu X, Zhang C, Yu P S. Joint structure feature exploration and regularization for multi-task graph classification. IEEE Transactions on Knowledge and Data Engineering, 2016, 28(3): 715–728

    Article  Google Scholar 

  34. Wang D, Cui P, Zhu W. Structural deep network embedding. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2016, 1225–1234

  35. You J, Ying R, Ren X, Hamilton W L, Leskovec J. GraphRNN: generating realistic graphs with deep auto-regressive models. In: Proceedings of the 35th International Conference on Machine Learning. 2018

  36. Wu Z, Pan S, Chen F, Long G, Zhang C, Yu P S. A comprehensive survey on graph neural networks. IEEE Transactions on Neural Networks and Learning Systems, 2021, 32(1): 4–24

    Article  MathSciNet  Google Scholar 

  37. Niepert M, Ahmed M, Kutzkov K. Learning convolutional neural networks for graphs. In: Proceedings of the 33rd International Conference on Machine Learning. 2016, 2014–2023

  38. Hamilton W L, Ying R, Leskovec J. Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. 2017, 1025–1035

  39. Veličković P, Cucurull G, Casanova A, Romero A, Liò P, Bengio Y. Graph attention networks. In: Proceedings of the 6th International Conference on Learning Representations. 2018

  40. He X, Deng K, Wang X, Li Y, Zhang Y, Wang M. LightGCN: simplifying and powering graph convolution network for recommendation. In: Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval. 2020, 639–648

  41. Wang X, He X, Cao Y, Liu M, Chua T S. KGAT: knowledge graph attention network for recommendation. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 2019, 950–958

  42. Zeng J, Wang X, Liu J, Chen Y, Liang Z, Chua T S, Chua Z L. SHADEWATCHER: recommendation-guided cyber threat analysis using system audit records. In: Proceedings of 2022 IEEE Symposium on Security and Privacy. 2022

  43. Lin H, Ma J, Cheng M, Yang Z, Chen L, Chen G. Rumor detection on twitter with claim-guided hierarchical graph attention networks. In: Proceedings of 2021 Conference on Empirical Methods in Natural Language Processing. 2021, 10035–10047

  44. van den Oord A, Kalchbrenner N, Vinyals O, Espeholt L, Graves A, Kavukcuoglu K. Conditional image generation with PixelCNN decoders. In: Proceedings of the 30th International Conference on Neural Information Processing Systems. 2016, 4797–4805

  45. Hjelm R D, Fedorov A, Lavoie-Marchildon S, Grewal K, Bachman P, Trischler A, Bengio Y. Learning deep representations by mutual information estimation and maximization. In: Proceedings of the 7th International Conference on Learning Representations. 2019

  46. Goodfellow I J, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, Courville A, Bengio Y. Generative adversarial nets. In: Proceedings of the 27th International Conference on Neural Information Processing Systems. 2014, 2672–2680

  47. Gutmann M, Hyvärinen A. Noise-contrastive estimation: a new estimation principle for unnormalized statistical models. In: Proceedings of the 13th International Conference on Artificial Intelligence and Statistics. 2010, 297–304

  48. Liu X, Zhang F, Hou Z, Mian L, Wang Z, Zhang J, Tang J. Self-supervised learning: generative or contrastive. IEEE Transactions on Knowledge and Data Engineering, 2021, doi: https://doi.org/10.1109/TKDE.2021.3090866

  49. van den Oord A, Li Y, Vinyals O. Representation learning with contrastive predictive coding. 2019, arXiv preprint arXiv: 1807.03748

  50. Tschannen M, Djolonga J, Rubenstein P K, Gelly S, Lucic M. On mutual information maximization for representation learning. In: Proceedings of the 8th International Conference on Learning Representations. 2020

  51. Caron M, Bojanowski P, Joulin A, Douze M. Deep clustering for unsupervised learning of visual features. In: Proceedings of the 15th European Conference on Computer Vision. 2018, 139–156

  52. Caron M, Misra I, Mairal J, Goyal P, Bojanowski P, Joulin A. Unsupervised learning of visual features by contrasting cluster assignments. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. 2020, 831

  53. Alwassel H, Mahajan D, Korbar B, Torresani L, Ghanem B, Tran D. Self-supervised learning by cross-modal audio-video clustering. In: Proceedings of the 34th International Conference on Neural Information Processing Systems. 2020, 818

  54. Veličković P, Fedus W, Hamilton W L, Liò P, Bengio Y, Hjelm R D. Deep graph infomax. In: Proceedings of the 7th International Conference on Learning Representations. 2019

  55. Kim D, Oh A. How to find your friendly neighborhood: graph attention design with self-supervision. In: Proceedings of the 9th International Conference on Learning Representations. 2021

  56. Wu J, Wang X, Feng F, He X, Chen L, Lian J, Xie X. Self-supervised graph learning for recommendation. In: Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval. 2020, 726–735

  57. Liu Y, Xu S, Tourassi G D. Detecting rumors through modeling information propagation networks in a social media environment. In: Proceedings of 8th International Conference, SBP 2015. 2015

  58. Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez A N, Kaiser Ł, Polosukhin I. Attention is all you need. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. 2017, 6000–6010

  59. Bello I, Zoph B, Le Q, Vaswani A, Shlens J. Attention augmented convolutional networks. In: Proceedings of 2019 IEEE/CVF International Conference on Computer Vision. 2019, 3285–3294

  60. Wang X, Girshick R, Gupta A, He K. Non-local neural networks. In: Proceedings of 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2018, 7794–7803

  61. Kim Y. Convolutional neural networks for sentence classification. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing. 2014, 1746–1751

  62. Zhang Z K, Liu C, Zhan X X, Lu X, Zhang C X, Zhang Y C. Dynamics of information diffusion and its applications on complex networks. Physics Reports, 2016, 651: 1–34

    Article  MathSciNet  Google Scholar 

  63. Huang Q, Zhou C, Wu J, Liu L, Wang B. Deep spatial—temporal structure learning for rumor detection on twitter. Neural Computing and Applications, 2020, doi: https://doi.org/10.1007/s00521-020-05236-4

  64. Wei L, Hu D, Zhou W, Yue Z, Hu S. Towards propagation uncertainty: edge-enhanced Bayesian graph convolutional networks for rumor detection. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing. 2021, 3845–3854

  65. Kingma D P, Ba J. Adam: a method for stochastic optimization. In: Proceedings of the 3rd International Conference on Learning Representations. 2015

  66. Loshchilov I, Hutter F. SGDR: stochastic gradient descent with warm restarts. In: Proceedings of the 5th International Conference on Learning Representations. 2017

  67. Mikolov T, Sutskever I, Chen K, Corrado G, Dean J. Distributed representations of words and phrases and their compositionality. In: Proceedings of the 26th International Conference on Neural Information Processing Systems. 2013, 3111–3119

  68. Glorot X, Bengio Y. Understanding the difficulty of training deep feedforward neural networks. In: Proceedings of the 13th International Conference on Artificial Intelligence and Statistics. 2010, 249–256

  69. Li Q, Han Z, Wu X M. Deeper insights into graph convolutional networks for semi-supervised learning. In: Proceedings of the 32th AAAI Conference on Artificial Intelligence. 2018, 3538–3545

  70. Wang F, Liu H. Understanding the behaviour of contrastive loss. In: Proceedings of 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2021, 2495–2504

  71. Farinneya P, Pour M M A, Hamidian S, Diab M. Active learning for rumor identification on social media. In: Proceedings of the Findings of the Association for Computational Linguistics: EMNLP 2021. 2021, 4556–4565

  72. Xia R, Xuan K, Yu J. A state-independent and time-evolving network for early rumor detection in social media. In: Proceedings of 2020 Conference on Empirical Methods in Natural Language Processing. 2020, 9042–9051

  73. Chami I, Ying R, Re C, Leskovec J. Hyperbolic graph convolutional neural networks. In: Proceedings of the 33rd International Conference on Neural Information Processing Systems. 2019, 438

  74. Wu Y X, Wang X, Zhang A, He X, Chua T S. Discovering invariant rationales for graph neural networks. In: Proceedings of the ICLR 2022. 2022

  75. Wang X, Wu Y X, Zhang A, He X, Chua T S. Towards multi-grained explainability for graph neural networks. In: Proceedings of the 34th Annual Conference on Neural Information Processing Systems. 2021

  76. Li Y, Wang X, Xiao J, Ji W, Chua T S. Invariant grounding for video question answering. 2022, arXiv preprint arXiv: 2206.02349

Download references

Acknowledgements

This work was supported by the National Key Research and Development Program of China (2020AAA0106000), the National Natural Science Foundation of China (Grant Nos. U21B2026, 62121002), and the CCCD Key Lab of Ministry of Culture and Tourism.

Author information

Authors and Affiliations

  1. School of Information Science and Technology, University of Science and Technology of China, Hefei, 230026, China

    Yuan Gao, Xiang Wang, Xiangnan He & Yongdong Zhang

  2. Beijing Electronic Science and Technology Institute, Beijing, 102627, China

    Huamin Feng

Authors
  1. Yuan Gao
    View author publications

    Search author on:PubMed Google Scholar

  2. Xiang Wang
    View author publications

    Search author on:PubMed Google Scholar

  3. Xiangnan He
    View author publications

    Search author on:PubMed Google Scholar

  4. Huamin Feng
    View author publications

    Search author on:PubMed Google Scholar

  5. Yongdong Zhang
    View author publications

    Search author on:PubMed Google Scholar

Corresponding authors

Correspondence to Xiang Wang or Xiangnan He.

Additional information

Yuan Gao received the MS degree in Electrical and Computer Engineering from University of Michigan, USA in 2019. He is now a PhD student in the School of Cyberspace Science and Technology at the University of Science and Technology of China (USTC), China. His research interest lies in fraud detection, representation learning, and graph learning.

Xiang Wang is now a professor at the University of Science and Technology of China (USTC), China. He received his PhD degree from National University of Singapore, Singapore in 2019. His research interests include recommender systems, graph learning, AI explainability, and AI security. He has published some academic papers on international conferences such as NeurIPS, ICLR, KDD, WWW, SIGIR, and AAAI. He serves as a program committee member for several top conferences such as SIGIR and WWW.

Xiangnan He is a professor at the University of Science and Technology of China (USTC), China. He received his PhD in Computer Science from the National University of Singapore (NUS), Singapore. His research interests span information retrieval, data mining, and multi-media analytics. He has over 80 publications that appeared in several top conferences such as SIGIR, WWW, and MM, and journals including TKDE, TOIS, and TMM. His work has received the Best Paper Award Honorable Mention in WWW 2018 and ACM SIGIR 2016. He is in the editorial board of journals including Frontiers in Big Data, AI Open. Moreover, he has served as the PC chair of CCIS 2019 and SPC/PC member for several top conferences including SIGIR, WWW, KDD, MM, WSDM, ICML, etc., and the regular reviewer for journals including TKDE, TOIS, TMM.

Huamin Feng is now a professor at Beijing Electronic Science and Technology Institute, China. He received his PhD dergree from National University of Singapore, Singapore in 2005. His research interests include multimedia semantic analysis, recommend system, and Web content analysis. He has published some academic papers on international conferences such as WWW, SIGIR, and MMM.

Yongdong Zhang (Senior Member, IEEE) received the PhD degree in electronic engineering from Tianjin University, China in 2002. He is currently a Professor with the University of Science and Technology of China. He has authored more than 100 refereed journal and conference papers. His current research interests include multimedia content analysis and understanding, multimedia content security, video encoding, and streaming media technology. He was a recipient of the Best Paper Award in PCM2013, ICIMCS 2013, and ICME 2010; and the Best Paper Candidate in ICME 2011. He serves as an Editorial Board Member for Multimedia Systems journal and Neurocomputing.

Electronic supplementary material

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Gao, Y., Wang, X., He, X. et al. Rumor detection with self-supervised learning on texts and social graph. Front. Comput. Sci. 17, 174611 (2023). https://doi.org/10.1007/s11704-022-1531-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s11704-022-1531-9

Keywords