Consistency Regularization on Unlabeled News:在无标注数据熵使用约束信号,让全局预测结果和局部预测结果尽可能的一致性高一些。 首先,将全局表征和局部表征加权求和,得到原型表征 之后拉近原型表征和全局表征以及局部表征之间的预测logits Training Objective and Fake News Detection:总的损失是有监督交叉熵损失和无监督约...
Tackling Fake News Detection by Continually Improving Social Context Representations using Graph Neural Networks 基于持续增强社交上下文表示的图神经网络虚假新闻检测 论文作者:Nikhil Mehta, Maria Leonor Pacheco, Dan Goldwasser 2022ACL 普渡大学 论文地址:https://aclanthology.org/2022.acl-long.97.pdf 代码和数...
代码地址:blakzaei/LOSS_GAT: Label Propagation and One-Class Semi-Supervised Graph Attention Network for Fake News Detection (github.com) Abstract 识别假新闻的一个重大挑战是带标签的新闻数据集有限。因此,仅利用来自兴趣类的一小部分标记数据的One-Class Learning(OCL)方法,可以是解决这一挑战的合适方法。
基于深度学习的多模态虚假新闻检测(Fake News Detection, FND)一直饱受关注,本文发现以往关于多模态FND的研究仍未解决两个主要问题: 不同工作虽提出一系列复杂的特征提取和跨模态融合网络来从新闻中获取表征判断是否存在异常。然而,没有足够的机制保证每个模态提取的信息都能够被充分用于最终的新闻检测决策环节,也鲜有显...
fake news detection综述【文章标题:深度综述:伪新闻检测】 1. 介绍 伪新闻是当今社会一个备受争议的话题,它的存在严重影响了信息的真实性和推送的可信度。为了解决这一问题,伪新闻检测技术应运而生。本文将从多个角度对伪新闻检测进行全面评估,并探讨其在当前信息社会中的重要性和挑战。 2. 伪新闻检测的定义 伪...
The fake news detection model can help curb the spread of fake news by acting as a tool for news organisations to check the authenticity of a news article.doi:10.1142/S0219649224500758Aditya BinayAnisha BinayJordan RegisterProfessor Suliman Hawamdeh...
Fake-News-Detection This project aims to classify Fake news in this sea of misconcepts and delusions. Looking at the explosion of social networks and with them the immense circulation of fake news, misguiding people into believing something which is unreal. ...
Fake news is not only harmful to individuals and society, but also to businesses and governments. For instance, fake news about the organization, which are emitted by spam or malicious users, can cause considerable damage. Therefore, fake news detection has become a significant research area. In...
bidirectional recurrent neural network fake news detection pytorch,如何实现双向循环神经网络(BidirectionalRecurrentNeuralNetwork)用于假新闻检测(PyTorch)##1.简介在本文中,我将向你展示如何使用PyTorch实现双向循环神经网络(BidirectionalRecurrentNeuralNetwo
Interpretable Fake News Detection with Graph Evidence Hao Guo, Weixin Zeng, Jiuyang Tang, Xiang Zhao 2023 Learn over Past, Evolve for Future: Forecasting Temporal Trends for Fake News Detection Beizhe Hu, Qiang Sheng, Juan Cao, Yongchun Zhu, Da...