Domain feature transfer-based multi-domain fake news detection

被引：0

作者：

Meng, Xuan ^{[1
]}

Zhao, Di ^{[1
,2
,3
]}

Meng, Jiana ^{[1
]}

Guo, Xu ^{[1
]}

Ma, Tengfei ^{[1
]}

Wang, Xiaopei ^{[4
]}

机构：

[1] Dalian Minzu Univ, Comp Sci & Engn, 18 Liaohe West Rd, Dalian 116600, Liaoning, Peoples R China

[2] Dalian Univ Technol, Sch Comp Sci & Technol, Dalian 116026, Liaoning, Peoples R China

[3] Dalian Yongjia Elect Technol Co Ltd, Postdoctoral Workstn, Dalian, Liaoning, Peoples R China

[4] Binzhou Polytech, Informat Engn Coll, 919 Huanghe 12th Rd, Binzhou 256603, Shandong, Peoples R China

来源：

KNOWLEDGE AND INFORMATION SYSTEMS | 2025年

基金：

中国国家自然科学基金;

关键词：

Fake news detection; Transfer learning; Multi-task learning; Multi-domain;

D O I：

10.1007/s10115-025-02412-7

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Fake news is spreading rapidly throughout social media, and is having serious negative consequences on both individuals and society. Currently, fake news detection methods often only predict news for a single domain, neglecting the domain information contained within the text. This may result in an inability to make effective predictions in domains where there is a low quantity or quality of data. With the aid of multi-task learning and transfer learning concepts, this paper presents a domain feature transfer-based multi-domain fake news detection (DFTD). First, we construct a multi-task feature extractor to obtain news text features in different domains. Then, we build an implicit domain gatherer to mine hidden domain information in the news. Next, the domain feature transferor is combined to obtain cross-domain text features. Finally, these features are inputted into the fake news detector for prediction. Our model maintains the extensive association information between domains while segmenting them. Additionally, it employs features from several source domains to aid in determining the authenticity of news in the target domain. Relevant experiments conducted on Weibo21 provide proof of the effectiveness of this model.

引用

页数：29

共 39 条

[1]

[Anonymous], 2014, P C EMP METH NAT LAN, DOI DOI 10.3115/V1/D14-1181

[2]

[Anonymous], 2016, P 25 INT JOINT C ART

[3]

Aribandi V, 2022, Ext5: towards extreme multi-task scaling for transfer learning

[4] MulT: An End-to-End Multitask Learning Transformer [J].

Bhattacharjee, Deblina ;

Zhang, Tong ;

Suesstrunk, Sabine ;

Salzmann, Mathieu .

2022 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR), 2022, :12021-12031

[5] Exploring Relational Context for Multi-Task Dense Prediction [J].

Bruggemann, David ;

Kanakis, Menelaos ;

Obukhov, Anton ;

Georgoulis, Stamatios ;

Van Gool, Luc .

2021 IEEE/CVF INTERNATIONAL CONFERENCE ON COMPUTER VISION (ICCV 2021), 2021, :15849-15858

[6]

Cheng M, 2020, VRoC: variational autoencoder-aided multi-task rumor classifier based on text

[7]

Devlin J, 2019, Arxiv, DOI [arXiv:1810.04805, DOI 10.48550/ARXIV.1810.04805]

[8] User Preference-aware Fake News Detection [J].

Dou, Yingtong ;

Shu, Kai ;

Xia, Congying ;

Yu, Philip S. ;

Sun, Lichao .

SIGIR '21 - PROCEEDINGS OF THE 44TH INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, 2021, :2051-2055

[9]

Dun Y, 2021, Kan: knowledge-aware attention network for fake news detection, P81

[10] TieFake: Title-Text Similarity and Emotion-Aware Fake News Detection [J].

Guo, Quanjiang ;

Kang, Zhao ;

Tian, Ling ;

Chen, Zhouguo .

2023 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS, IJCNN, 2023,

← 1 2 3 4 →