Evidence-aware Bayesian neural networks for fake news detection

CHEN Junhai; XIANG Fengtao; LI Tuoxin; LUO Xiangyu

doi:10.11959/j.issn.2096-6652.202533

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Evidence-aware Bayesian neural networks for fake news detection

Papers and Reports | 更新时间：2025-11-18

- Evidence-aware Bayesian neural networks for fake news detection
- Chinese Journal of Intelligent Science and Technology Vol. 7, Issue 3, Pages: 316-328(2025)
- 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：
- 基金信息：
  
  The National Natural Science Foundation of China(62473371)
- DOI：10.11959/j.issn.2096-6652.202533
  CLC： TP391.4
- Received：07 April 2025，
  
  Revised：2025-07-07，
  
  Accepted：17 July 2025，
  
  Published：15 September 2025
- 稿件说明：
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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.

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references

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