基于可解释图神经网络模型的社交媒体谣言识别研究

doi:10.3772/j.issn.1000-0135.2023.11.010

情报学报

2023, Vol. 42

Issue (11): 1369-1381 DOI: 10.3772/j.issn.1000-0135.2023.11.010

情报技术与应用

本期目录 | 过刊浏览 | 高级检索

基于可解释图神经网络模型的社交媒体谣言识别研究

汪子航, 言鹏韦, 蒋卓人

浙江大学公共管理学院信息资源管理系，杭州 310058

Interpretable Graph Neural Network for Social Media Rumor Detection

Wang Zihang, Yan Pengwei, Jiang Zhuoren

Department of Information Resources Management, School of Public Affairs, Zhejiang University, Hangzhou 310058

摘要
图/表
参考文献
相关文章 (15)

全文: PDF (4014 KB) HTML (1 KB)
输出: BibTeX | EndNote (RIS)

摘要随着社交媒体数据规模的增长与数据形式的复杂化，社交媒体谣言识别研究面临新的挑战。一方面，谣言传播网络中的复杂结构特征难以被充分挖掘；另一方面，亟须探索基于深度神经网络的谣言识别模型的可解释性。本文设计和实现了具备可解释性的图神经网络模型应用于谣言识别任务。具体而言，本文在运用残差图卷积神经网络模型进行谣言识别的基础上，进一步训练基于掩码学习的图神经网络解释器，不仅将谣言传播网络结构特征纳入识别模型，而且从传播网络结构和传播节点属性两个视角对图神经网络模型自动生成解释。本文基于新浪微博（中文）和推特（英文）来源的两个网络谣言数据集进行实验，并从全局与个案两个层面进行解释性分析。研究结果显示，本文提出的图神经网络模型可以有效利用谣言传播网络结构特征，在谣言识别任务中的表现超过了一系列对照组模型。结合图神经网络解释器生成的解释可以发现，在较大规模的谣言传播树中，长传播链条是谣言的关键网络拓扑结构；在规模较小的谣言传播树中，文本特征是关键的节点属性。

	服务

	把本文推荐给朋友
	加入我的书架
	加入引用管理器
	E-mail Alert
	RSS
	作者相关文章
	汪子航
	言鹏韦
	蒋卓人

关键词 ：社交网络, 谣言识别, 图神经网络, 模型可解释性

收稿日期: 2022-08-29

基金资助:国家自然科学基金青年科学基金项目“基于图神经网络的政策舆情演化机制研究”（72104212）；浙江省自然科学基金项目“基于深度表示学习的政策舆情动态画像研究”（LY22G030002）；中央高校基本科研业务费专项资金资助项目。

作者简介: 汪子航，男，2000年生，硕士研究生，主要研究方向为计算社会科学；言鹏韦，男，1996年生，博士研究生，主要研究方向为社交网络、图深度学习、可解释学习；蒋卓人，通信作者，男，1986年生，博士，百人计划研究员，博士生导师，主要研究方向为社交媒体挖掘、自然语言处理、计算社会科学等，E-mail：jiangzhuoren@zju.edu.cn；

引用本文:

汪子航, 言鹏韦, 蒋卓人. 基于可解释图神经网络模型的社交媒体谣言识别研究[J]. 情报学报, 2023, 42(11): 1369-1381.
Wang Zihang, Yan Pengwei, Jiang Zhuoren. Interpretable Graph Neural Network for Social Media Rumor Detection. 情报学报, 2023, 42(11): 1369-1381.

链接本文:

https://qbxb.istic.ac.cn/CN/10.3772/j.issn.1000-0135.2023.11.010 或 https://qbxb.istic.ac.cn/CN/Y2023/V42/I11/1369

1 Bondielli A, Marcelloni F. A survey on fake news and rumour detection techniques[J]. Information Sciences, 2019, 497: 38-55.
2 DiFonzo N, Bordia P. Rumor, gossip and urban legends[J]. Diogenes, 2007, 54(1): 19-35.
3 Zubiaga A, Liakata M, Procter R, et al. Towards detecting rumours in social media[C]// Proceedings of the 29th AAAI Conference on Artificial Intelligence. Palo Alto: AAAI Press, 2015: 35-41.
4 贺刚, 吕学强, 李卓, 等. 微博谣言识别研究[J]. 图书情报工作, 2013, 57(23): 114-120.
5 Castillo C, Mendoza M, Poblete B. Information credibility on Twitter[C]// Proceedings of the 20th International Conference on World Wide Web. New York: ACM Press, 2011: 675-684.
6 Kwon S, Cha M, Jung K, et al. Prominent features of rumor propagation in online social media[C]// Proceedings of the 2013 IEEE 13th International Conference on Data Mining. Piscataway: IEEE, 2013: 1103-1108.
7 曾子明, 王婧. 基于LDA和随机森林的微博谣言识别研究——以2016年雾霾谣言为例[J]. 情报学报, 2019, 38(1): 89-96.
8 Yu F, Liu Q A, Wu S, et al. A convolutional approach for misinformation identification[C]// Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence. Palo Alto: AAAI Press, 2017: 3901-3907.
9 Wang W Y. “Liar, liar pants on fire”: a new benchmark dataset for fake news detection[C]// Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics. Stroudsburg: Association for Computational Linguistics, 2017: 422-426.
10 Afroz S, Brennan M, Greenstadt R. Detecting hoaxes, frauds, and deception in writing style online[C]// Proceedings of the 2012 IEEE Symposium on Security and Privacy. Piscataway: IEEE, 2012: 461-475.
11 Liu X M, Nourbakhsh A, Li Q Z, et al. Real-time rumor debunking on Twitter[C]// Proceedings of the 24th ACM International on Conference on Information and Knowledge Management. New York: ACM Press, 2015: 1867-1870.
12 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. New York: ACM Press, 2015: 1751-1754.
13 Bruna J, Zaremba W, Szlam A, et al. Spectral networks and locally connected networks on graphs[C]// Proceedings of the 2nd International Conference on Learning Representations. ICLR, 2014: 1-14.
14 Kipf T N, Welling M. Semi-supervised classification with graph convolutional networks[C]// Proceedings of the 5th International Conference on Learning Representations. ICLR, 2017: 1-14.
15 Hamilton W L, Ying R, Leskovec J. Inductive representation learning on large graphs[C]// Proceedings of the 31st International Conference on Neural Information Processing Systems. Red Hook: Curran Associates, 2017: 1025-1035.
16 Veli?kovi? P, Cucurull G, Casanova A, et al. Graph attention networks[C]// Proceedings of the 6th International Conference on Learning Representations. ICLR, 2018: 1-12.
17 Yan S J, Xiong Y J, Lin D H. Spatial temporal graph convolutional networks for skeleton-based action recognition[C]// Proceedings of the 32nd AAAI Conference on Artificial Intelligence. Palo Alto: AAAI Press, 2018: 7444-7452.
18 Bian T A, Xiao X, Xu T Y, et al. Rumor detection on social media with bi-directional graph convolutional networks[C]// Proceedings of the 34th AAAI Conference on Artificial Intelligence. Palo Alto: AAAI Press, 2020: 549-556.
19 Bai N, Meng F R, Rui X B, et al. Rumour detection based on graph convolutional neural net[J]. IEEE Access, 2021, 9: 21686-21693.
20 王昕岩, 宋玉蓉, 宋波. 一种加权图卷积神经网络的新浪微博谣言检测方法[J]. 小型微型计算机系统, 2021, 42(8): 1780-1786.
21 Ribeiro M T, Singh S, Guestrin C. “Why should I trust you?”: explaining the predictions of any classifier[C]// Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York: ACM Press, 2016: 1135-1144.
22 Zhou B L, Khosla A, Lapedriza A, et al. Learning deep features for discriminative localization[C]// Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE, 2016: 2921-2929.
23 Selvaraju R R, Cogswell M, Das A, et al. Grad-CAM: visual explanations from deep networks via gradient-based localization[C]// Proceedings of the 16th IEEE International Conference on Computer Vision. Piscataway: IEEE, 2017: 618-626.
24 Chattopadhyay A, Sarkar A, Howlader P, et al. Grad-CAM++: generalized gradient-based visual explanations for deep convolutional networks[C]// Proceedings of the 2018 IEEE Winter Conference on Applications of Computer Vision. Piscataway: IEEE, 2018: 839-847.
25 Zhang J M, Bargal S A, Lin Z, et al. Top-down neural attention by excitation backprop[J]. International Journal of Computer Vision, 2018, 126(10): 1084-1102.
26 Bach S, Binder A, Montavon G, et al. On pixel-wise explanations for non-linear classifier decisions by layer-wise relevance propagation[J]. PLoS One, 2015, 10(7): e0130140.
27 Galhotra S, Pradhan R, Salimi B. Explaining black-box algorithms using probabilistic contrastive counterfactuals[C]// Proceedings of the 2021 International Conference on Management of Data. New York: ACM Press, 2021: 577-590.
28 Pope P E, Kolouri S, Rostami M, et al. Explainability methods for graph convolutional neural networks[C]// Proceedings of the 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE, 2020: 10764-10773.
29 Huang Q, Yamada M, Tian Y, et al. GraphLIME: local interpretable model explanations for graph neural networks[J]. IEEE Transactions on Knowledge and Data Engineering, 2023, 35(7): 6968-6972.
30 Vu M N, Thai M T. PGM-Explainer: probabilistic graphical model explanations for graph neural networks[C]// Proceedings of the 34th International Conference on Neural Information Processing Systems. Red Hook: Curran Associates, 2020: 12225-12235.
31 Ying R, Bourgeois D, You J X, et al. GNNExplainer: generating explanations for graph neural networks[C]// Proceedings of the 33rd International Conference on Neural Information Processing Systems. Red Hook: Curran Associates, 2019: 9244-9255.
32 Luo D S, Cheng W, Xu D K, et al. Parameterized explainer for graph neural network[C]// Proceedings of the 34th International Conference on Neural Information Processing Systems. Red Hook: Curran Associates, 2020: 19620-19631.
33 Li G H, Müller M, Thabet A, et al. DeepGCNs: can GCNs go as deep as CNNs?[C]// Proceedings of the 2019 IEEE/CVF International Conference on Computer Vision. Piscataway: IEEE, 2020: 9266-9275.
34 Song C H, Tu C C, Yang C, et al. CED: credible early detection of social media rumors[OL]. (2018-11-10). https://arxiv.org/pdf/1811.04175.pdf.
35 Kochkina E, Liakata M, Zubiaga A. PHEME dataset for rumour detection and veracity classification[DS/OL]. (2018-06-10). https://doi.org/10.6084/m9.figshare.6392078.v1.
36 Wellman B. Culture of the Internet[M]. Marva: Lawrence Erlbaum Associates Publishers, 1997: 179-205.
37 Ni J M, Abrego G H, Constant N, et al. Sentence-T5: scalable sentence encoders from pre-trained text-to-text models[C]// Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics. Stroudsburg: Association for Computational Linguistics, 2022: 1864-1874.
38 Vapnik V N, Chervonenkis A. A note on one class of perceptrons[J]. Automation and Remote Control, 1964, 25(12): 821-837.
39 Breiman L. Random forests[J]. Machine Language, 2001, 45(1): 5-32.
40 Chen T Q, Guestrin C. XGBoost: a scalable tree boosting system[C]// Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York: ACM Press, 2016: 785-794.
41 McCallum A, Nigam K. A comparison of event models for naive Bayes text classification[C]// Proceedings of the AAAI-98 Workshop on Learning for Text Categorization. Palo Alto: AAAI Press, 1998: 41-48.
42 Shu K, Wang S H, Liu H. Understanding user profiles on social media for fake news detection[C]// Proceedings of the 2018 IEEE Conference on Multimedia Information Processing and Retrieval. Piscataway: IEEE, 2018: 430-435.
43 Prasad J. The psychology of rumour: a study relating to the great Indian earthquake of 1934[J]. British Journal of Psychology General Section, 1935, 26(1): 1-15.
44 刘于思, 徐煜. 在线社会网络中的谣言与辟谣信息传播效果: 探讨网络结构因素与社会心理过程的影响[J]. 新闻与传播研究, 2016, 23(11): 51-69, 127.
45 van der Maaten L, Hinton G. Visualizing data using t-SNE[J]. Journal of Machine Learning Research, 2008, 9: 2579-2605.
46 Kullback S, Leibler R A. On information and sufficiency[J]. The Annals of Mathematical Statistics, 1951, 22(1): 79-86.