智能科学与技术学报2025，Vol.7Issue(3)：316-328,13.DOI:10.11959/j.issn.2096-6652.202533
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
摘要
Abstract
The popularity of social media has led to accelerated fake news propagation and expanded influence.The ex-tensive 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 de-tection(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 ef-fectively 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 effi-ciency and robustness.It not only provides a new research perspective for the field of rumor detection but also offers a vi-able technical solution to address uncertainty issues in information dissemination.关键词
贝叶斯神经网络/变分推理/无偏估计/虚假信息检测Key words
Bayesian neural networks/variational inference/unbiased estimation/fake news detection分类
信息技术与安全科学引用本文复制引用
陈君海,项凤涛,黎拓新,罗翔宇..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,2025,7(3):316-328,13.基金项目
国家自然科学基金项目(No.62473371)The National Natural Science Foundation of China(No.62473371) （No.62473371）
