Detecting Rumor Based on Graph Convolution Network and Attention Mechanism
Feng Lizhou1,Liu Furong1,Wang Youwei2()
1School of Statistics, Tianjin University of Finance and Economics, Tianjin 300222, China 2School of Information, Central University of Finance and Economics, Beijing 100081, China
[Objective] This paper proposes a rumor detection method based on a graph convolutional network and attention mechanism, which utilizes comment forwarding and text semantic features. [Methods] Firstly, we analyzed the forwarding and replying relationship among comments and constructed a comment relationship feature map to explore the comments’ correlations. Then, we used the BERT model to generate the vector representation of sentences based on their text semantic similarity. We also built the semantic feature map of comments by calculating the cosine similarity and fully extracting their semantic relevance. Third, we completed information dissemination among nodes based on a Graph Convolutional Network (GCN). We also used the attention mechanism to distinguish the impact of original and other comments on rumor detection. Finally, we obtained an accurate representation of the comment nodes. [Results] Our model’s accuracy on the Twitter15 and Twitter16 public datasets reached 0.860 and 0.870, with F1 mean values of 0.858 and 0.866. Compared with the BiGCN method, our model’s accuracy improved by 5.1% and 1.5% on the Twitter15 and Twitter16 datasets, and the F1 mean improved by 5.0% and 1.9%, respectively. [Limitation] We only used texts for rumor detection. Future research will combine images, user attributes, and time attributes to improve the model’s accuracy. [Conclusion] The proposed method can effectively improve the performance of rumor detection, providing valuable references for related tasks.
凤丽洲, 刘馥榕, 王友卫. 基于图卷积网络和注意力机制的谣言检测方法*[J]. 数据分析与知识发现, 2024, 8(4): 125-136.
Feng Lizhou, Liu Furong, Wang Youwei. Detecting Rumor Based on Graph Convolution Network and Attention Mechanism. Data Analysis and Knowledge Discovery, 2024, 8(4): 125-136.
(Chen Yanfang, Li Zhiyu, Liang Xun, et al. Review on Rumor Detection of Online Social Networks[J]. Chinese Journal of Computers, 2018, 41(7): 1648-1677.)
(Gao Yujun, Liang Gang, Jiang Fangting, et al. Social Network Rumor Detection: A Survey[J]. Acta Electronica Sinica, 2020, 48(7): 1421-1435.)
doi: 10.3969/j.issn.0372-2112.2020.07.023
[3]
Castillo C, Mendoza M, Poblete B. Information Credibility on Twitter[C]// Proceedings of the 20th International Conference on World Wide Web. 2011: 675-684.
(Ma Ming, Liu Yun, Liu Dijun, et al. Rumor Detection in Microblogs Based on Topic and Prevention Model[J]. Transactions of Beijing Institute of Technology, 2020, 40(3): 310-315.)
[5]
Kwon S, Cha M, Jung K, et al. Prominent Features of Rumor Propagation in Online Social Media[C]// Proceedings of 2013 IEEE 13th International Conference on Data Mining. 2013: 1103-1108.
[6]
Ma J, Gao W, Mitra P, et al. Detecting Rumors from Microblogs with Recurrent Neural Networks[C]// Proceedings of the 25th International Joint Conference on Artificial Intelligence. 2016: 3818-3824.
(Liu Jinshuo, Feng Kuo, Jeff Z.Pan, et al. MSRD: Multi-Modal Web Rumor Detection Method[J]. Journal of Computer Research and Development, 2020, 57(11): 2328-2336.)
[8]
Luo Z R, Li Q Q, Zheng J. Deep Feature Fusion for Rumor Detection on Twitter[J]. IEEE Access, 2021, 9: 126065-126074.
[9]
Shelhamer E, Long J, Darrell T. Fully Convolutional Networks for Semantic Segmentation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017, 39(4): 640-651.
doi: 10.1109/TPAMI.2016.2572683
pmid: 27244717
[10]
Kim Y. Convolutional Neural Networks for Sentence Classification[C]// Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing. 2014: 1746-1751.
[11]
Zhang X, Zhao J B, LeCun Y. Character-Level Convolutional Networks for Text Classification[C]// Proceedings of the 28th International Conference on Neural Information Processing Systems - Volume 1. 2015: 649-657.
[12]
Peng Y, Wang J M. Rumor Detection Based on Attention CNN and Time Series of Context Information[J]. Future Internet, 2021, 13(11): 267.
[13]
Ma J, Gao W, Wong K F. Rumor Detection on Twitter with Tree-Structured Recursive Neural Networks[C]// Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1:Long Papers). 2018: 1980-1989.
(Yin Pengbo, Pan Weimin, Peng Cheng, et al. Research on Early Detection of Weibo Rumors Based on User Characteristics Analysis[J]. Journal of Intelligence, 2020, 39(7): 81-86.)
[15]
Umer M, Imtiaz Z, Ullah S, et al. Fake News Stance Detection Using Deep Learning Architecture(CNN-LSTM)[J]. IEEE Access, 2020, 8: 156695-156706.
[16]
Kipf T N, Welling M. Semi-supervised Classification with Graph Convolutional Networks[OL]. arXiv Preprint, arXiv: 1609.02907.
(Wang Youwei, Tong Shuang, Feng Lizhou, et al. New Inductive Microblog Rumor Detection Method Based on Graph Convolutional Network[J]. Journal of Zhejiang University (Engineering Science), 2022, 56(5): 956-966.)
[18]
Bian T, Xiao X, Xu T Y, et al. Rumor Detection on Social Media with Bidirectional Graph Convolutional Networks[C]// Proceedings of the 34th AAAI Conference on Artificial Intelligence. 2020.
(Wang Xinyan, Song Yurong, Song Bo. Sina Microblog Rumor Detection Method Based on Weighted-Graph Convolutional Network[J]. Journal of Chinese Computer Systems, 2021, 42(8): 1780-1786.)
[20]
Zhang Y F, Yu X L, Cui Z Y, et al. Every Document Owns Its Structure: Inductive Text Classification via Graph Neural Networks[C]// Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. 2020: 334-339.
(Ye Zicheng, Yan Guiying. Study on Keyword Extraction Algorithm Based on Graphical Model[J]. Journal of Systems Science and Mathematical Sciences, 2021, 41(4): 967-975.)
[22]
Devlin J, Chang M W, Lee K, et al. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding[C]// Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics:Human Language Technologies, Volume 1 (Long and Short Papers). 2019: 4171-4186.
[23]
Battaglia P W, Hamrick J B, Bapst V, et al. Relational Inductive Biases, Deep Learning, and Graph Networks[OL]. arXiv Preprint, arXiv: 1806.01261.
[24]
Ma J, Gao W, Wong K F. Detect Rumors in Microblog Posts Using Propagation Structure Via Kernel Learning[C]// Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1:Long Papers). 2017: 708-717.
[25]
Zhang H L, Alim M A, Li X, et al. Misinformation in Online Social Networks[J]. ACM Transactions on Information Systems, 2016, 34(3): 18.
[26]
Ma J, Gao W, Wei Z Y, et al. Detect Rumors Using Time Series of Social Context Information on Microblogging Websites[C]// Proceedings of the 24th ACM International on Conference on Information and Knowledge Management. 2015: 1751-1754.
[27]
Liu Y, Wu Y F. Early Detection of Fake News on Social Media Through Propagation Path Classification with Recurrent and Convolutional Networks[C]// Proceedings of the 32nd AAAI Conference on Artificial Intelligence. 2018.
[28]
Chen Y H, Yin C Y, Zuo W L. Multi-task Learning for Stance and Early Rumor Detection[J]. Optical Memory and Neural Networks, 2021, 30(2): 131-139.
[29]
Chen X N, Wang C Y, Li D, et al. A New Early Rumor Detection Model Based on BiGRU Neural Network[J]. Discrete Dynamics in Nature and Society, 2021, 2021: 2296605.
