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Graph global attention network with memory: A deep learning approach for fake news detection

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成果类型:
期刊论文
作者:
Chang, Qian;Li, Xia;Duan, Zhao
通讯作者:
Li, X
作者机构:
[Duan, Zhao; Li, Xia; Chang, Qian] Cent China Normal Univ, Sch Informat Management, Wuhan, Peoples R China.
通讯机构:
[Li, X ] C
Cent China Normal Univ, Sch Informat Management, Wuhan, Peoples R China.
语种:
英文
关键词:
Fake news detection;Graph classification;Graph convolutional networks;Social network
期刊:
Neural Networks
ISSN:
0893-6080
年:
2024
卷:
172
页码:
106115
基金类别:
CRediT authorship contribution statement Qian Chang: Conceptualization, Methodology, Software, Validation, Writing – original draft, Writing – review & editing. Xia Li: Conceptualization, Methodology, Writing – review & editing, acquisition, Supervision. Zhao Duan: Methodology, Writing – review & editing, Supervision.
机构署名:
本校为第一且通讯机构
院系归属:
信息管理学院
摘要:
With the proliferation of social media, the detection of fake news has become a critical issue that poses a significant threat to society. The dissemination of fake information can lead to social harm and damage the credibility of information. To address this issue, deep learning has emerged as a promising approach, especially with the development of Natural Language Processing (NLP). This study introduces a novel approach called Graph Global Attention Network with Memory (GANM) for detecting fake news. This approach leverages NLP techniques to encode nodes with news context and user content. ...

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