融合证据分析的贝叶斯神经网络虚假信息检测方法

陈君海; 项凤涛; 黎拓新; 罗翔宇

doi:10.11959/j.issn.2096-6652.202533

您当前的位置：

首页 >

文章列表页 >

融合证据分析的贝叶斯神经网络虚假信息检测方法

学术论文 | 更新时间：2025-11-18

- 融合证据分析的贝叶斯神经网络虚假信息检测方法
- Evidence-aware Bayesian neural networks for fake news detection
- 智能科学与技术学报 2025年7卷第3期页码：316-328
- 作者机构：
  
  国防科技大学智能科学学院，湖南长沙 410073
- 作者简介：
  
  [ "陈君海（2001- ），男，国防科技大学智能科学学院硕士生，主要研究方向为人工智能、真假鉴别。" ]
  [ "项凤涛（1986- ），男，博士，国防科技大学智能科学学院副教授，主要研究方向为智能辅助决策、不确定性推理、智能控制。" ]
  [ "黎拓新（2002- ），男，国防科技大学智能科学学院硕士生，主要研究方向为模式识别人工智能、迁移学习。" ]
  [ "罗翔宇（2002- ），男，国防科技大学智能科学学院硕士生，主要研究方向为人工智能、时空学习。" ]
- 基金信息：
  
  国家自然科学基金项目(62473371)
- DOI：10.11959/j.issn.2096-6652.202533
  中图分类号： TP391.4
- 收稿：2025-04-07，
  
  修回：2025-07-07，
  
  录用：2025-07-17，
  
  纸质出版：2025-09-15
- 稿件说明：
移动端阅览
陈君海,项凤涛,黎拓新等.融合证据分析的贝叶斯神经网络虚假信息检测方法[J].智能科学与技术学报,2025,07(03):316-328.

CHEN Junhai,XIANG Fengtao,LI Tuoxin,et al.Evidence-aware Bayesian neural networks for fake news detection[J].Chinese Journal of Intelligent Science and Technology,2025,07(03):316-328.
陈君海,项凤涛,黎拓新等.融合证据分析的贝叶斯神经网络虚假信息检测方法[J].智能科学与技术学报,2025,07(03):316-328. DOI： 10.11959/j.issn.2096-6652.202533.

CHEN Junhai,XIANG Fengtao,LI Tuoxin,et al.Evidence-aware Bayesian neural networks for fake news detection[J].Chinese Journal of Intelligent Science and Technology,2025,07(03):316-328. DOI： 10.11959/j.issn.2096-6652.202533.

摘要

社交媒体的普及带来了虚假信息扩散速度加快、影响范围扩大等问题，虚假信息的广泛传播不仅会扰乱社会秩序，还可能引发群体性事件，对国家安全和社会稳定构成潜在威胁，研究高效的虚假信息检测工具与技术变得尤为重要。基于此，提出了融合证据分析的贝叶斯神经网络虚假信息检测（evidence-aware Bayesian neural networks for fake news detection，EBNN-FND）方法，该方法借助贝叶斯神经网络框架，对检测模型与数据的不确定性进行量化分析，从而提升预测结果的可靠性。模型设计了文本嵌入模块、特征处理模块、观点-证据交互模块和特征混合模块，能够充分整合信息文本与关联证据信息的特征。在公共数据集上的实验表明，EBNN-FND模型在虚假信息检测任务中的表现显著优于现有基线模型，具有高效性与稳定性，不仅为虚假信息检测领域提供了新的研究视角，也为解决信息传播过程中的不确定性问题提供了一种可行的技术方案。

Abstract

The popularity of social media has led to accelerated fake news propagation and expanded influence. The extensive spread of fake news not only disrupts social order but may also trigger mass incidents

posing a potential threat to national security and social stability. Consequently

the development of efficient fake news detection tools and techniques has become increasingly critical. To address this challenge

an evidence-aware Bayesian neural networks for fake news detection (EBNN-FND) method was proposed. This model quantifies uncertainties in both the detection model and the data

thereby improving the reliability of prediction results. The EBNN-FND model consists of four modules: a text embedding module

a feature processing module

a news-evidence interaction module

and a feature fusion module. Thereby

it can effectively integrate the features of news context and related evidence. Experiments on public datasets demonstrate that the EBNN-FND model significantly outperforms existing baseline models in fake news detection tasks

showcasing its efficiency and robustness. It not only provides a new research perspective for the field of rumor detection but also offers a viable technical solution to address uncertainty issues in information dissemination.

关键词

Keywords

references

CARANNANTE G, BOUAYNAYA N C. Bayesian deep learning detection of anomalies and failure: application to medical images[C]//Proceedings of the 2023 IEEE 33rd International Workshop on Machine Learning for Signal Processing (MLSP). Piscataway: IEEE Press, 2023: 1-6.

MA R H, ZHANG H, ZHANG J, et al. Bayesian uncertainty modeling for P300-based brain-computer interface[J]. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2023, 31: 2789-2799.

XUAN J Y, LU J, ZHANG G Q. A survey on Bayesian nonparametric learning[J]. ACM Computing Surveys, 2020, 52(1): 1-36.

许旻辰, 屈丹, 司念文, 等. 社交媒体虚假信息检测技术研究综述[J]. 计算机工程, 2025, doi: 10.19678/j.issn.1000-3428.0070287.

XU M C, QU D, SI N W, et al. A survey of the technologies for detecting disinformation in social media[J]. Computer Engineering, 2025, doi: 10.19678/j.issn.1000-3428.0070287.

张元园, 袁嘉霁. 基于社交媒体的谣言检测研究综述[J]. 数据通信, 2024(1): 28-33.

ZHANG Y Y, YUAN J J. A review of rumor detection research based on social media[J]. Data Communications, 2024(1): 28-33.

张欣, 孙靖超. 基于大语言模型的虚假信息检测框架综述[J]. 计算机科学与探索, 2025, 19(6): 1414-1436.

ZHANG X, SUN J C. Review of false information detection frameworks based on large language models[J]. Journal of Frontiers of Computer Science and Technology, 2025, 19(6): 1414-1436.

袁唯淋, 赵卫伟, 胡振震, 等. 智能情报融合综述: 对抗视角下的开源情报融合分析[J]. 智能科学与技术学报, 2024, 6(3): 284-300.

YUAN W L, ZHAO W W, HU Z Z, et al. Research on intelligence fusion: a holistic analysis of open-source intelligence fusion from the perspective of confrontation[J]. Chinese Journal of Intelligent Science and Technology, 2024, 6(3): 284-300.

刘宇栋, 黄千里, 王恒, 等. 基于数据增强的多模态虚假信息检测框架研究[J]. 信息安全研究, 2025, 11(4): 377-384.

LIU Y D, HUANG Q L, WANG H, et al. Research on data-enhanced multi-modal false information detection framework[J]. Journal of Information Security Research, 2025, 11(4): 377-384.

徐绪堪, 李溢, 李一铭. 融合传播路径和多模态特征的社交媒体信息可信度模型构建[J]. 情报理论与实践, 2025, 48(5): 168-176.

XU X K, LI Y, LI Y M. Construction of a social media information credibility model that integrates propagation paths and multi-modal features[J]. Information Studies (Theory & Application), 2025, 48(5): 168-176.

LIU Q, YU F, WU S, et al. Mining significant microblogs for misinformation identification[J]. ACM Transactions on Intelligent Systems and Technology, 2018, 9(5): 1-20.

CHANDRA S, MISHRA P, YANNAKOUDAKIS H, et al. Graph-based modeling of online communities for fake news detection[EB]. arXiv preprint, 2020, arXiv: 2008.06274.

ZHANG X Y, CAO J, LI X R, et al. Mining dual emotion for fake news detection[C]//Proceedings of the Web Conference 2021. New York: ACM, 2021: 3465-3476.

张仰森, 彭媛媛, 段宇翔, 等. 基于评论异常度的新浪微博谣言识别方法[J]. 自动化学报, 2020, 46(8): 1689-1702.

ZHANG Y S, PENG Y Y, DUAN Y X, et al. The method of sina weibo rumor detecting based on comment abnormality[J]. Acta Automatica Sinica, 2020, 46(8): 1689-1702.

XU W Z, WU J F, LIU Q, et al. Evidence-aware fake news detection with graph neural networks[C]//Proceedings of the ACM Web Conference 2022. New York: ACM, 2022: 2501-2510.

WU J F, LIU Q, XU W Z, et al. Bias mitigation for evidence-aware fake news detection by causal intervention[C]//Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval. New York: ACM, 2022: 2308-2313.

CASTILLO C, MENDOZA M, POBLETE B. Information credibility on twitter[C]//Proceedings of the 20th International Conference on World Wide Web. New York: ACM, 2011: 675-684.

MA J, GAO W, WEI Z Y, et al. Detect rumors using time series of social context information on microblogging[C]//Proceedings of the 24th ACM International on Conference on Information and Knowledge Manageme. New York: ACM, 2017: 1751-1754.

GIASEMIDIS G, SINGLETON C, AGRAFIOTIS I, et al. Determining the veracity of rumours on twitter[C]//International Conference on Social Informatics. Cham: Springer, 2016: 185-205.

MA J, GAO W, MITRA P, et al. Detecting rumors from microblogs with recurrent neural networks[C]//Proceedings of the 25th International Conference on World Wide Web. New York: ACM, 2016: 1861-1872.

RUCHANSKY N, SEO S, LIU Y. CSI: a hybrid deep model for fake news detection[C]//Proceedings of the 2017 ACM on Conference on Information and Knowledge Management. New York: ACM, 2017: 797-806.

JIN Z W, CAO J, GUO H, et al. Multimodal fusion with recurrent neural networks for rumor detection on microblogs[C]//Proceedings of the 25th ACM International Conference on Multimedia. New York: ACM, 2017: 795-816.

KUMAR S, ASTHANA R, UPADHYAY S, et al. Fake news detection using deep learning models: a novel approach[J]. Transactions on Emerging Telecommunications Technologies, 2020, 31(2): e3767.

ABDULLAH A A, HASSAN M M, MUSTAFA Y T. A review on Bayesian deep learning in healthcare: applications and challenges[J]. IEEE Access, 2022, 10: 36538-36562.

GHAHRAMANI Z. Probabilistic machine learning and artificial intelligence[J]. Nature, 2015, 521(7553): 452-459.

HÜLLERMEIER E, WAEGEMAN W. Aleatoric and epistemic uncertainty in machine learning: an introduction to concepts and methods[J]. Machine Learning, 2021, 110(3): 457-506.

DEVLIN J, CHANG M W, LEE K, et al. Bert: pre-training of deep bidirectional transformers for language understanding[EB]. arXiv preprint, 2019, arXiv: 1810.04805.

KIM Y. Convolutional neural networks for sentence classification[C]//Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP). Stroudsburg: ACL, 2014: 1746-1751.

XUE B Y, HU S K, XU J H, et al. Bayesian neural network language modeling for speech recognition[J]. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 2022, 30: 2900-2917.

DUAN H P, YANG L X, FANG J, et al. Fast inverse-free sparse Bayesian learning via relaxed evidence lower bound maximization[J]. IEEE Signal Processing Letters, 2017, 24(6): 774-778.

BLUNDELL C, CORNEBISE J, KAVUKCUOGLU K, et al. Weight uncertainty in neural networks[J]. 32nd International Conference on Machine Learning, ICML 2015, 2015, 2: 1613-1622.

XUE B Y, YU J W, XU J H, et al. Bayesian transformer language models for speech recognition[C]//Proceedings of the ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). Piscataway: IEEE Press, 2021: 7378-7382.

KINGMA D P, WELLING M. Auto-encoding variational bayes[EB]. arXiv preprint, 2014, arXiv: 1312.6114.

XU C, KECHADI M T. Fuzzy deep hybrid network for fake news detection[C]//Proceedings of the 12th International Symposium on Information and Communication Technology. New York: ACM, 2023: 118-125.

HANSEN C, HANSEN C, CHAVES LIMA L. Automatic fake news detection: are models learning to reason? [C]//Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers). Stroudsburg: ACL, 2021: 80-86.

WANG W Y. "Liar, liar pants on fire": a new benchmark dataset for fake news detection[EB]. arXiv preprint, 2017, arXiv: 1705.00648.

HANSEN C, HANSEN C, LIMA L C. Automatic fake news detection: are models learning to reason? [EB]. arXiv preprint, 2021, arXiv: 2105.07698.

POPAT K, MUKHERJEE S, YATES A, et al. DeClarE: debunking fake news and false claims using evidence-aware deep learning[C]//Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing. Stroudsburg: ACL, 2018: 22-32.

KARIMI H, ROY P, SABA-SADIYA S, et al. Multi-source multi-class fake news detection[C]// Proceedings of the 27th International Conference on Computational Linguistics. Stroudsburg: ACL, 2018: 1546-1557.

GUO J Y, DU L, BI W D, et al. Homophily-oriented heterogeneous graph rewiring[C]//Proceedings of the ACM Web Conference 2023. New York: ACM, 2023: 511-522.

LIU C, WU X H, YU M, et al. A two-stage model based on BERT for short fake news detection[C]//International Conference on Knowledge Science, Engineering and Management. Cham: Springer International Publishing, 2019: 172-183.

LONG Y, LU Q, XIANG R, et al. Fake news detection through multi-perspective speaker profiles[C]//Proceedings of the Eighth International Joint Conference on Natural Language Processing. Stroudsburg: ACL, 2017: 252-256.

XU C, KECHADI M T. An enhanced fake news detection system with fuzzy deep learning[J]. IEEE Access, 2024, 12: 88006-88021.

浏览量

162

下载量

CSCD

文章被引用时，请邮件提醒。

提交

工具集

关联资源

Cerberus：基于深度学习的跨站社交网络机器人检测系统