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Rumor Detection on Social Media with Graph Adversarial Contrastive Learning

Published: 25 April 2022 Publication History

Abstract

Rumors spread through the Internet, especially on Twitter, have harmed social stability and residents’ daily lives. Recently, in addition to utilizing the text features of posts for rumor detection, the structural information of rumor propagation trees has also been valued. Most rumors with salient features can be quickly locked by graph models dominated by cross entropy loss. However, these conventional models may lead to poor generalization, and lack robustness in the face of noise and adversarial rumors, or even the conversational structures that is deliberately perturbed (e.g., adding or deleting some comments). In this paper, we propose a novel Graph Adversarial Contrastive Learning (GACL) method to fight these complex cases, where the contrastive learning is introduced as part of the loss function for explicitly perceiving differences between conversational threads of the same class and different classes. At the same time, an Adversarial Feature Transformation (AFT) module is designed to produce conflicting samples for pressurizing model to mine event-invariant features. These adversarial samples are also used as hard negative samples in contrastive learning to make the model more robust and effective. Experimental results on three public benchmark datasets prove that our GACL method achieves better results than other state-of-the-art models.

References

[1]
Tian Bian, Xi Xiao, Tingyang Xu, Peilin Zhao, Wenbing Huang, Yu Rong, and Junzhou Huang. 2020. Rumor detection on social media with bi-directional graph convolutional networks. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 34. 549–556.
[2]
Hengyi Cai, Hongshen Chen, Yonghao Song, Zhuoye Ding, Yongjun Bao, Weipeng Yan, and Xiaofang Zhao. 2020. Group-wise contrastive learning for neural dialogue generation. arXiv preprint arXiv:2009.07543(2020).
[3]
Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton. 2020. A simple framework for contrastive learning of visual representations. In International conference on machine learning. PMLR, 1597–1607.
[4]
Xinlei Chen and Kaiming He. 2021. Exploring simple siamese representation learning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 15750–15758.
[5]
Bo Dai and Dahua Lin. 2017. Contrastive learning for image captioning. arXiv preprint arXiv:1710.02534(2017).
[6]
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2018. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805(2018).
[7]
Tianyu Gao, Xingcheng Yao, and Danqi Chen. 2021. SimCSE: Simple Contrastive Learning of Sentence Embeddings. arXiv preprint arXiv:2104.08821(2021).
[8]
Zhiwei Jin, Juan Cao, Han Guo, Yongdong Zhang, and Jiebo Luo. 2017. Multimodal fusion with recurrent neural networks for rumor detection on microblogs. In Proceedings of the 25th ACM international conference on Multimedia. 795–816.
[9]
Nikhil Ketkar. 2017. Introduction to pytorch. In Deep learning with python. Springer, 195–208.
[10]
Prannay Khosla, Piotr Teterwak, Chen Wang, Aaron Sarna, Yonglong Tian, Phillip Isola, Aaron Maschinot, Ce Liu, and Dilip Krishnan. 2020. Supervised contrastive learning. arXiv preprint arXiv:2004.11362(2020).
[11]
Thomas N Kipf and Max Welling. 2016. Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907(2016).
[12]
Quanzhi Li, Qiong Zhang, and Luo Si. 2019. eventAI at SemEval-2019 task 7: Rumor detection on social media by exploiting content, user credibility and propagation information. In Proceedings of the 13th International Workshop on Semantic Evaluation. 855–859.
[13]
Quanzhi Li, Qiong Zhang, and Luo Si. 2019. Rumor detection by exploiting user credibility information, attention and multi-task learning. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. 1173–1179.
[14]
Weiyang Liu, Yandong Wen, Zhiding Yu, and Meng Yang. 2016. Large-margin softmax loss for convolutional neural networks. In ICML, Vol. 2. 7.
[15]
Yi-Ju Lu and Cheng-Te Li. 2020. GCAN: Graph-aware co-attention networks for explainable fake news detection on social media. arXiv preprint arXiv:2004.11648(2020).
[16]
Jing Ma, Wei Gao, Prasenjit Mitra, Sejeong Kwon, Bernard J Jansen, Kam-Fai Wong, and Meeyoung Cha. 2016. Detecting rumors from microblogs with recurrent neural networks. (2016).
[17]
Jing Ma, Wei Gao, Zhongyu Wei, Yueming Lu, and Kam-Fai Wong. 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. 1751–1754.
[18]
Jing Ma, Wei Gao, and Kam-Fai Wong. 2017. Detect rumors in microblog posts using propagation structure via kernel learning. Association for Computational Linguistics.
[19]
Jing Ma, Wei Gao, and Kam-Fai Wong. 2018. Detect rumor and stance jointly by neural multi-task learning. In Companion proceedings of the the web conference 2018. 585–593.
[20]
Jing Ma, Wei Gao, and Kam-Fai Wong. 2018. Rumor detection on twitter with tree-structured recursive neural networks. Association for Computational Linguistics.
[21]
Jing Ma, Wei Gao, and Kam-Fai Wong. 2019. Detect rumors on twitter by promoting information campaigns with generative adversarial learning. In The World Wide Web Conference. 3049–3055.
[22]
Dat Quoc Nguyen, Thanh Vu, and Anh Tuan Nguyen. 2020. BERTweet: A pre-trained language model for English Tweets. arXiv preprint arXiv:2005.10200(2020).
[23]
Lahari Poddar, Wynne Hsu, Mong Li Lee, and Shruti Subramaniyam. 2018. Predicting stances in twitter conversations for detecting veracity of rumors: A neural approach. In 2018 IEEE 30th International Conference on Tools with Artificial Intelligence (ICTAI). IEEE, 65–72.
[24]
Jiezhong Qiu, Qibin Chen, Yuxiao Dong, Jing Zhang, Hongxia Yang, Ming Ding, Kuansan Wang, and Jie Tang. 2020. Gcc: Graph contrastive coding for graph neural network pre-training. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 1150–1160.
[25]
Kai Shu, Suhang Wang, and Huan Liu. 2017. Exploiting tri-relationship for fake news detection. arXiv preprint arXiv:1712.07709 8 (2017).
[26]
Chi Sun, Xipeng Qiu, Yige Xu, and Xuanjing Huang. 2019. How to fine-tune bert for text classification?. In China National Conference on Chinese Computational Linguistics. Springer, 194–206.
[27]
NGUYEN VAN HA, K Sugiyama, P Nakov, and MY Kan. 2020. FANG: Leveraging Social Context for Fake News Detection Using Graph Representation. (2020).
[28]
Penghui Wei, Nan Xu, and Wenji Mao. 2019. Modeling conversation structure and temporal dynamics for jointly predicting rumor stance and veracity. arXiv preprint arXiv:1909.08211(2019).
[29]
Hanlu Wu, Tengfei Ma, Lingfei Wu, Tariro Manyumwa, and Shouling Ji. 2020. Unsupervised reference-free summary quality evaluation via contrastive learning. arXiv preprint arXiv:2010.01781(2020).
[30]
Lianwei Wu, Yuan Rao, Yongqiang Zhao, Hao Liang, and Ambreen Nazir. 2020. DTCA: Decision tree-based co-attention networks for explainable claim verification. arXiv preprint arXiv:2004.13455(2020).
[31]
Yuanmeng Yan, Rumei Li, Sirui Wang, Fuzheng Zhang, Wei Wu, and Weiran Xu. 2021. ConSERT: A Contrastive Framework for Self-Supervised Sentence Representation Transfer. arXiv preprint arXiv:2105.11741(2021).
[32]
Xiaoyu Yang, Yuefei Lyu, Tian Tian, Yifei Liu, Yudong Liu, and Xi Zhang. 2020. Rumor Detection on Social Media with Graph Structured Adversarial Learning. In IJCAI. 1417–1423.
[33]
Chun-Hsiao Yeh, Cheng-Yao Hong, Yen-Chi Hsu, Tyng-Luh Liu, Yubei Chen, and Yann LeCun. 2021. Decoupled Contrastive Learning. arXiv preprint arXiv:2110.06848(2021).
[34]
Yuning You, Tianlong Chen, Yongduo Sui, Ting Chen, Zhangyang Wang, and Yang Shen. 2020. Graph contrastive learning with augmentations. Advances in Neural Information Processing Systems 33 (2020), 5812–5823.
[35]
Feng Yu, Qiang Liu, Shu Wu, Liang Wang, Tieniu Tan, 2017. A Convolutional Approach for Misinformation Identification. In IJCAI. 3901–3907.
[36]
Chunyuan Yuan, Qianwen Ma, Wei Zhou, Jizhong Han, and Songlin Hu. 2019. Jointly embedding the local and global relations of heterogeneous graph for rumor detection. In 2019 IEEE International Conference on Data Mining (ICDM). IEEE, 796–805.
[37]
Zhilu Zhang and Mert R Sabuncu. 2018. Generalized cross entropy loss for training deep neural networks with noisy labels. In 32nd Conference on Neural Information Processing Systems (NeurIPS).
[38]
Yanqiao Zhu, Yichen Xu, Feng Yu, Qiang Liu, Shu Wu, and Liang Wang. 2021. Graph contrastive learning with adaptive augmentation. In Proceedings of the Web Conference 2021. 2069–2080.
[39]
Arkaitz Zubiaga, Maria Liakata, and Rob Procter. 2017. Exploiting context for rumour detection in social media. In International Conference on Social Informatics. Springer, 109–123.

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  • (2025)Label-aware learning to enhance unsupervised cross-domain rumor detectionJournal of Network and Computer Applications10.1016/j.jnca.2024.104084235(104084)Online publication date: Mar-2025
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  • (2025)Do not wait: Preemptive rumor detection with cooperative LLMs and accessible social contextInformation Processing & Management10.1016/j.ipm.2024.10399562:3(103995)Online publication date: May-2025
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      cover image ACM Conferences
      WWW '22: Proceedings of the ACM Web Conference 2022
      April 2022
      3764 pages
      ISBN:9781450390965
      DOI:10.1145/3485447
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      Published: 25 April 2022

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      Author Tags

      1. adversarial learning
      2. contrastive learning
      3. graph representation
      4. rumor detection
      5. social networks

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      April 25 - 29, 2022
      Virtual Event, Lyon, France

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      Overall Acceptance Rate 1,899 of 8,196 submissions, 23%

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      Cited By

      View all
      • (2025)Label-aware learning to enhance unsupervised cross-domain rumor detectionJournal of Network and Computer Applications10.1016/j.jnca.2024.104084235(104084)Online publication date: Mar-2025
      • (2025)A unified framework for multi-modal rumor detection via multi-level dynamic interaction with evolving stancesInformation Processing & Management10.1016/j.ipm.2025.10406662:3(104066)Online publication date: May-2025
      • (2025)Do not wait: Preemptive rumor detection with cooperative LLMs and accessible social contextInformation Processing & Management10.1016/j.ipm.2024.10399562:3(103995)Online publication date: May-2025
      • (2024)MVACLNet: A Multimodal Virtual Augmentation Contrastive Learning Network for Rumor DetectionAlgorithms10.3390/a1705019917:5(199)Online publication date: 8-May-2024
      • (2024)Rumor detection based on Attention Graph Adversarial Dual Contrast LearningPLOS ONE10.1371/journal.pone.029029119:4(e0290291)Online publication date: 22-Apr-2024
      • (2024)Contrastive Learning with Edge‐Wise Augmentation for Rumor DetectionInternational Journal of Intelligent Systems10.1155/2024/38585262024:1Online publication date: 9-Aug-2024
      • (2024)Towards Robust Rumor Detection with Graph Contrastive and Curriculum LearningACM Transactions on Knowledge Discovery from Data10.1145/365302318:7(1-21)Online publication date: 19-Jun-2024
      • (2024)Propagation Structure-Aware Graph Transformer for Robust and Interpretable Fake News DetectionProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3672024(4652-4663)Online publication date: 25-Aug-2024
      • (2024)Evolving to the Future: Unseen Event Adaptive Fake News Detection on Social MediaProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3679919(4273-4277)Online publication date: 21-Oct-2024
      • (2024)Semantic Evolvement Enhanced Graph Autoencoder for Rumor DetectionProceedings of the ACM Web Conference 202410.1145/3589334.3645478(4150-4159)Online publication date: 13-May-2024
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