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A Novel Approach for Rumor Detection in Social Platforms: Memory-Augmented Transformer with Graph Convolutional Networks

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成果类型:
期刊论文
作者:
Qian Chang;Xia Li*;Zhao Duan
通讯作者:
Xia Li
作者机构:
[Qian Chang; Xia Li; Zhao Duan] School of Information Management, Central China Normal University, Wuhan, China
通讯机构:
[Xia Li] S
School of Information Management, Central China Normal University, Wuhan, China
语种:
英文
期刊:
Knowledge-Based Systems
ISSN:
0950-7051
年:
2024
页码:
111625
基金类别:
CRediT authorship contribution statement Qian Chang: Conceptualization, Data curation, Formal analysis, 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:
Rumor detection in social media platforms is of critical importance owing to the widespread dissemination and impact of false information. Conventional approaches to rumor detection frequently rely on labor-intensive manual fact-checking or handcrafted features that may not adequately account for the complex nature of rumor propagation. To overcome these limitations, recent studies in deep learning, such as the recurrent neural network-based method and natural language processing techniques, have shown promise in capturing sequential patterns and analyzing textual content. However, these appro...

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