Please wait a minute...
Data Analysis and Knowledge Discovery  2024, Vol. 8 Issue (6): 82-94    DOI: 10.11925/infotech.2096-3467.2023.0542
Current Issue | Archive | Adv Search |
Hypergraph-Based Rumor Detection Model Integrating User Propagation Bias Information
Peng Jingjie,Gu Yijun(),Zhang Lanze
College of Information and Cyber Security, People’s Public Security University of China, Beijing 100038, China
Download: PDF (1822 KB)   HTML ( 4 ) 
Export: BibTeX | EndNote (RIS)      
Abstract  

[Objective] This paper aims to construct a tweet interaction hypergraph-based rumor detection model that integrates user propagation bias information to improve the accuracy of rumor detection. [Methods] A rumor detection model named UPBI_HGRD is proposed. The model integrates the user propagation bias information when obtaining the tweet node embedding representation, and constructs hyperedges based on user IDs to form a hypergraph that can reflect the interactive relationship of tweets. In addition, this paper proposes a tweet node-user hyperedge level multi-layer dual-level multi-head attention mechanism to focus on important tweet relationships, so as to effectively learn the embedding representation of nodes, and finally input it into a classifier to judge whether it is a rumor or not. [Results] The experimental results on three publicly available datasets show that the accuracy of the model reaches 94.57%, 97.82% and 94.76%, respectively, which is better than the existing baseline model, and has an excellent performance in early detection of rumors, which proves the effectiveness of the model. [Limitations] The limitation of the model in this paper is that the process of obtaining the tweet embedding representation that integrates the user propagation bias information and constructing the hypergraph has a certain time overhead. In the future, further research will be done to improve the time efficiency of the model. [Conclusions] The proposed method effectively improves the accuracy of rumor detection and provides a novel approach to identifying online rumors.
Key words： Rumor Detection      Node Embedding      User Propagation Bias Information      Hypergraph      Multi-layer Dual-level Multi-head Attention Mechanism     
Received: 07 June 2023      Published: 08 January 2024
ZTFLH:  TP183  
  G35  
Fund:Double First-Class Innovation Research Project for People’s Public Security University of China(2023SYL07)
Corresponding Authors: Gu Yijun，E-mail：guyijun@ppsuc.edu.cn。   
Service
E-mail this article
Add to my bookshelf
Add to citation manager
E-mail Alert
RSS
Articles by authors
Jingjie Peng
Yijun Gu
Lanze Zhang

Cite this article:

Peng Jingjie, Gu Yijun, Zhang Lanze. Hypergraph-Based Rumor Detection Model Integrating User Propagation Bias Information. Data Analysis and Knowledge Discovery, 2024, 8(6): 82-94.

URL:

https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.2096-3467.2023.0542     OR     https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2024/V8/I6/82

UPBI_HGRD Model Structure
Construct Hypergraph Based on User ID
数据集 推文传播子图总数 谣言事件数量 非谣言事件数量 节点总数 边总数 事件平均时间跨度/h
Politifact 314 157 157 41 054 40 740 21 094
Gossipcop 5 464 2 732 2 732 314 262 308 798 4 633
Weibo 2 078 1 039 1 039 279 177 277 099 1 096
Data Statistics
模型 Politifact Gossipcop Weibo
准确率/% F1值/% 时间/ms 准确率/% F1值/% 时间/ms 准确率/% F1值/% 时间/ms
Bi-GCN 77.38 77.27 1 363 87.04 86.91 15 102 89.67 89.60 13 516
GNN-CL 68.78 68.65 234 94.22 94.20 1 973 85.93 85.79 1 665
GCNFN 81.45 81.33 113 92.42 92.38 1 526 90.36 90.33 1 396
UPFD-SAGE 82.81 82.72 122 95.87 95.85 1 317 92.79 92.68 1 209
HGAT 82.80 82.71 5 667 OOM OOM OOM OOM OOM OOM
HGNN 81.89 81.78 1 161 93.27 93.23 10 565 90.08 89.96 8 945
UPBI_HGRD 94.57 94.56 336 97.82 97.79 3 648 94.76 94.71 2 898
Experimental Results of Rumor Detection on Politifact，Gossipcop and Weibo Datasets
模型 Politifact Gossipcop Weibo
准确率 F1值 准确率 F1值 准确率 F1值
w/o UPBI 85.97 85.95 95.54 95.49 91.57 91.48
w/o MLDMHA 84.16 84.08 95.30 95.26 90.89 90.78
UPBI_HGRD 94.57 94.56 97.82 97.79 94.76 94.71
Comparison of Ablation Experiment Results
Experimental Results of Early Detection on Politifact，Gossipcop and Weibo Datasets
[1] Chandra S, Mishra P, Yannakoudakis H, et al. Graph-Based Modeling of Online Communities for Fake News Detection[OL]. arXiv Preprint, arXiv: 2008.06274.
[2] Zhou X Y, Wu J D, Zafarani R. SAFE: Similarity-Aware Multi-modal Fake News Detection[C]// Proceedings of Pacific-Asia Conference on Knowledge Discovery and Data Mining. 2020: 354-367.
[3] Kaliyar R K, Goswami A, Narang P. FakeBERT: Fake News Detection in Social Media with a BERT-Based Deep Learning Approach[J]. Multimedia Tools and Applications, 2021, 80(8): 11765-11788.
[4] Zhang Y, Wallace B. A Sensitivity Analysis of (and Practitioners’ Guide to) Convolutional Neural Networks for Sentence Classification[OL]. arXiv Preprint, arXiv:1510.03820.
[5] Devlin J, Chang M W, Lee K, et al. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding[OL]. arXiv Preprint, arXiv:1810.04805.
[6] Sun L C, Hashimoto K, Yin W P, et al. Adv-BERT: BERT is not Robust on Misspellings! Generating Nature Adversarial Samples on BERT[OL]. arXiv Preprint, arXiv:2003.04985.
[7] Sun L C, Xia C Y, Yin W P, et al. Mixup-Transformer: Dynamic Data Augmentation for NLP Tasks[OL]. arXiv Preprint, arXiv:2010.02394.
[8] Monti F, Frasca F, Eynard D, et al. Fake News Detection on Social Media Using Geometric Deep Learning[OL]. arXiv Preprint, arXiv:1902.06673.
[9] Han Y, Karunasekera S, Leckie C. Graph Neural Networks with Continual Learning for Fake News Detection from Social Media[OL]. arXiv Preprint, arXiv:2007.03316.
[10] Kipf T N, Welling M. Semi-supervised Classification with Graph Convolutional Networks[OL]. arXiv Preprint, arXiv:1609.02907.
[11] Nickerson R S. Confirmation Bias: A Ubiquitous Phenomenon in Many Guises[J]. Review of general psychology, 1998, 2(2): 175-220.
[12] Yang F, Liu Y, Yu X H, et al. Automatic Detection of Rumor On sina Weibo[C]// Proceedings of the ACM SIGKDD Workshop on Mining Data Semantics. 2012: 1-7.
[13] Castillo C, Mendoza M, Poblete B. Information Credibility on Twitter[C]// Proceedings of the 20th International Conference on World Wide Web. 2011: 675-684.
[14] 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.
[15] 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.
[16] Zhao Z, Resnick P, Mei Q Z. Enquiring Minds: Early Detection of Rumors in Social Media from Enquiry Posts[C]// Proceedings of the 24th International Conference on World Wide Web. 2015: 1395-1405.
[17] Yang Y Q. COVID-19 Fake News Detection via Graph Neural Networks in Social Media[C]// Proceedings of 2021 IEEE International Conference on Bioinformatics and Biomedicine. 2021: 3178-3180.
[18] Yuan C Y, Ma Q W, Zhou W, et al. Jointly Embedding the Local and Global Relations of Heterogeneous Graph for Rumor Detection[C]// Proceedings of 2019 IEEE International Conference on Data Mining. 2019: 796-805.
[19] Ran H Y, Jia C Y, Zhang P F, et al. MGAT-ESM: Multi-channel Graph Attention Neural Network with Event-Sharing Module for Rumor Detection[J]. Information Sciences, 2022, 592: 402-416.
[20] 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.
[21] Khoo L M S, Chieu H L, Qian Z, et al. Interpretable Rumor Detection in Microblogs by Attending to User Interactions[C]// Proceedings of the 34th AAAI Conference on Artificial Intelligence. 2020: 8783-8790.
[22] Bian T, Xiao X, Xu T, et al. Rumor Detection on Social Media with Bi-directional Graph Convolutional Networks[C]// Proceedings of the 34th AAAI Conference on Artificial Intelligence. 2020: 549-556.
[23] Ma J, Gao W. Debunking Rumors on Twitter with Tree Transformer[C]// Proceedings of the 28th International Conference on Computational Linguistics. 2020: 5455-5466.
[24] Wu Z Y, Pi D C, Chen J F, et al. Rumor Detection Based on Propagation Graph Neural Network with Attention Mechanism[J]. Expert Systems with Applications, 2020, 158: 113595.
[25] Tu K F, Chen C, Hou C Y, et al. Rumor2vec: A Rumor Detection Framework with Joint Text and Propagation Structure Representation Learning[J]. Information Sciences, 2021, 560: 137-151.
[26] Luo Z R, Li Q Q, Zheng J. Deep Feature Fusion for Rumor Detection on Twitter[J]. IEEE Access, 2021, 9: 126065-126074.
[27] Wei L W, Hu D, Zhou W, et al. Towards Propagation Uncertainty: Edge-Enhanced Bayesian Graph Convolutional Networks for Rumor Detection[OL]. arXiv Preprint, arXiv: 2107.11934.
[28] Ren Y X, Zhang J W. Fake News Detection on News-Oriented Heterogeneous Information Networks Through Hierarchical Graph Attention[C]// Proceedings of 2021 International Joint Conference on Neural Networks. 2021: 1-8.
[29] 强子珊, 顾益军. 基于多模态异质图的社交媒体谣言检测模型[J]. 数据分析与知识发现, 2023, 7(11): 68-78.
[29] (Qaing Zishan, Gu Yijun. Detecting Social Media Rumors Based on Multimodal Heterogeneous Graph[J]. Data Analysis and Knowledge Discovery, 2023, 7(11): 68-78.)
[30] 韩晓鸿, 赵梦凡, 张钰涛. 联合异质图卷积网络和注意力机制的假新闻检测[J]. 小型微型计算机系统, 2024, 45(2): 301-308.
[30] (Han Xiaohong, Zhao Mengfan, Zhang Yutao. Joint Heterogeneous Graph Convolutional Network and Attention Mechanism for Fake News Detection[J]. Journal of Chinese Computer Systems, 2024, 45(2): 301-308.)
[31] Dou Y T, Shu K, Xia C Y, et al. User Preference-Aware Fake News Detection[C]// Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval. 2021: 2051-2055.
[32] Jiang J W, Wei Y X, Feng Y F, et al. Dynamic Hypergraph Neural Networks[C]// Proceedings of the 28th International Joint Conference on Artificial Intelligence. 2019: 2635-2641.
[33] Feng Y F, You H X, Zhang Z Z, et al. Hypergraph Neural Networks[C]// Proceedings of the 33th AAAI Conference on Artificial Intelligence. 2019: 3558-3565.
[34] Jeong U, Ding K Z, Cheng L, et al. Nothing Stands Alone: Relational Fake News Detection with Hypergraph Neural Networks[OL]. arXiv Preprint, arXiv: 2212.12621.
[35] Ding K Z, Wang J L, Li J D, et al. Be More with Less: Hypergraph Attention Networks for Inductive Text Classification[OL]. arXiv Preprint, arXiv: 2011.00387.
[36] Shu K, Mahudeswaran D, Wang S H, et al. FakeNewsNet: A Data Repository with News Content, Social Context, and Spatiotemporal Information for Studying Fake News on Social Media[J]. Big Data, 2020, 8(3): 171-188.
[37] 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.
[38] Ying R, You J X, Morris C, et al. Hierarchical Graph Representation Learning with Differentiable Pooling[C]// Proceedings of the 32nd International Conference on Neural Information Processing Systems. 2018: 4805-4815.
[39] Godbole S, Sarawagi S. Discriminative Methods for Multi-labeled Classification[C]// Proceedings of Pacific-Asia Conference on Knowledge Discovery and Data Mining. 2004: 22-30.
[1] Feng Lizhou, Liu Furong, Wang Youwei. Detecting Rumor Based on Graph Convolution Network and Attention Mechanism[J]. 数据分析与知识发现, 2024, 8(4): 125-136.
[2] He Guoxiu, Ren Jiayu, Li Zongyao, Lin Chenxi, Yu Haiyan. Revisiting Deep Learning-based Rumor Detection Models with Interpretable Tools[J]. 数据分析与知识发现, 2024, 8(4): 1-13.
[3] Huang Xuejian, Ma Tinghuai, Wang Gensheng. Detecting Weibo Rumors Based on Hierarchical Semantic Feature Learning Model[J]. 数据分析与知识发现, 2023, 7(5): 81-91.
[4] Chen Wenjie. Scientific Collaboration Recommendation Based on Hypergraph[J]. 数据分析与知识发现, 2023, 7(4): 68-76.
[5] Qiang Zishan, Gu Yijun. Detecting Social Media Rumors Based on Multimodal Heterogeneous Graph[J]. 数据分析与知识发现, 2023, 7(11): 68-78.
[6] Zeng Ziming, Zhang Yu. Rumor Detection of Public Health Emergencies Based on Data Augmentation and Multi-Task Learning[J]. 数据分析与知识发现, 2023, 7(11): 56-67.
[7] Meng Jiana, Wang Xiaopei, Li Ting, Liu Shuang, Zhao Di. Cross-Modal Rumor Detection Based on Adversarial Neural Network[J]. 数据分析与知识发现, 2022, 6(12): 32-42.
[8] Bai Simeng,Niu Zhendong,He Hui,Shi Kaize,Yi Kun,Ma Yuanchi. Biomedical Text Classification Method Based on Hypergraph Attention Network[J]. 数据分析与知识发现, 2022, 6(11): 13-24.
[9] Yang Hanxun, Zhou Dequn, Ma Jing, Luo Yongcong. Detecting Rumors with Uncertain Loss and Task-level Attention Mechanism[J]. 数据分析与知识发现, 2021, 5(7): 101-110.
[10] Kan Liu,Haochen Du. Detecting Twitter Rumors with Deep Transfer Network[J]. 数据分析与知识发现, 2019, 3(10): 47-55.
[11] Shou Huanrong,Deng Shuqing,Xu Jian. Detecting Online Rumors with Sentiment Analysis[J]. 数据分析与知识发现, 2017, 1(7): 44-51.
  Copyright © 2016 Data Analysis and Knowledge Discovery   Tel/Fax:(010)82626611-6626，82624938   E-mail:jishu@mail.las.ac.cn