Tensor Factorization With Sparse and Graph Regularization for Fake News Detection on Social Networks

Che, H; Pan, B; Leung, MF; Cao, Y; Yan, Z

Tensor Factorization With Sparse and Graph Regularization for Fake News Detection on Social Networks

Che, H Pan, B Leung, MF Cao, Y Yan, Z

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Publisher:: IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Publication Type:: Journal Article
Citation:: IEEE Transactions on Computational Social Systems, 2023, PP, (99)
Issue Date:: 2023-01-01

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Field	Value	Language
dc.contributor.author	Che, H
dc.contributor.author	Pan, B
dc.contributor.author	Leung, MF
dc.contributor.author	Cao, Y
dc.contributor.author	Yan, Z https://orcid.org/0000-0003-3368-2100
dc.date.accessioned	2024-03-18T04:01:06Z
dc.date.available	2024-03-18T04:01:06Z
dc.date.issued	2023-01-01
dc.identifier.citation	IEEE Transactions on Computational Social Systems, 2023, PP, (99)
dc.identifier.issn	2329-924X
dc.identifier.issn	2329-924X
dc.identifier.uri	http://hdl.handle.net/10453/176848
dc.description.abstract	Social media has a significant influence, which greatly facilitates people to stay up-to-date with information. Unfortunately, a great deal of fake news on social media misleads people and causes a lot of losses. Therefore, fake news detection is necessary to address this issue. Recently, social content category-based methods have become a crucial component of fake news detection. Different from news context-based category, which focuses on word embedding, it tends to explore the potential relationships and structures between users and news. In this article, a third-order tensor, which obtains massive information and connections, is constructed by the social links and engagements of social networks. Then, a sparse and graph-regularized CANDECOMP/PARAFAC (SGCP) tensor decomposition learning method is proposed for fake news detection on social network. In SGCP, a news factor matrix is constructed by CP decomposition of the tensor, which reflects the complex connections among users and news. Furthermore, SGCP retains sparsity of the news factor matrix and preserves the manifold structures from the original space. In addition, an efficient optimization algorithm, which is proven to be monotonically nonincreasing, is proposed to solve SGCP. Finally, abundant experiments are conducted on real-world datasets and demonstrate the effectiveness of the proposed SGCP.
dc.language	English
dc.publisher	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
dc.relation.ispartof	IEEE Transactions on Computational Social Systems
dc.relation.isbasedon	10.1109/TCSS.2023.3296479
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	Tensor Factorization With Sparse and Graph Regularization for Fake News Detection on Social Networks
dc.type	Journal Article
utslib.citation.volume	PP
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	University of Technology Sydney/Strength - AAII - Australian Artificial Intelligence Institute
utslib.copyright.status	closed_access	*
dc.date.updated	2024-03-18T04:00:57Z
pubs.issue	99
pubs.publication-status	Published
pubs.volume	PP
utslib.citation.issue	99

Abstract:

Social media has a significant influence, which greatly facilitates people to stay up-to-date with information. Unfortunately, a great deal of fake news on social media misleads people and causes a lot of losses. Therefore, fake news detection is necessary to address this issue. Recently, social content category-based methods have become a crucial component of fake news detection. Different from news context-based category, which focuses on word embedding, it tends to explore the potential relationships and structures between users and news. In this article, a third-order tensor, which obtains massive information and connections, is constructed by the social links and engagements of social networks. Then, a sparse and graph-regularized CANDECOMP/PARAFAC (SGCP) tensor decomposition learning method is proposed for fake news detection on social network. In SGCP, a news factor matrix is constructed by CP decomposition of the tensor, which reflects the complex connections among users and news. Furthermore, SGCP retains sparsity of the news factor matrix and preserves the manifold structures from the original space. In addition, an efficient optimization algorithm, which is proven to be monotonically nonincreasing, is proposed to solve SGCP. Finally, abundant experiments are conducted on real-world datasets and demonstrate the effectiveness of the proposed SGCP.

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http://hdl.handle.net/10453/176848