A Multi-View Framework for Fake News Detection Utilizing Dynamic User Propagation Structures, Temporal Changes, and Personal Attributes

Liu, Nan; Zhang, Fengli; Zhu, Ye; Wang, Ruijing; Tang, Chao; Pei, Xikai

A Multi-View Framework for Fake News Detection Utilizing Dynamic User Propagation Structures, Temporal Changes, and Personal Attributes

https://hdl.handle.net/10779/DRO/DU:30596009

conference contribution

posted on 2025-11-12, 23:08 authored by Nan Liu, Fengli Zhang, Ye ZhuYe Zhu, Ruijing Wang, Chao Tang, Xikai Pei

A Multi-View Framework for Fake News Detection Utilizing Dynamic User Propagation Structures, Temporal Changes, and Personal Attributes

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DOI - Is version of https://doi.org/10.1007/978-981-96-0847-8_17

Location

Sydney, N.S.W.

Open access

No

Language

eng

Editor/Contributor(s)

Sheng QZ, Jiang J, Zhang WE, Wu J, Ma C, Dobbie G, Zhang X, Manolopoulos Y, Mansoor W

Volume

15391

Pagination

243-257

Start date

2024-12-03

End date

2024-12-05

ISSN

2945-9133

eISSN

1611-3349

ISBN-13

978-981-96-0846-1

Title of proceedings

ADMA 2024 : Proceedings of the 20th International Advanced Data Mining and Applications Conference

Event

Advanced Data Mining and Applications. Conference (2024 : 20th : Sydney, N.S.W)

Publisher

Springer

Series

Lecture Notes in Artificial Intelligence

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Keywords

Computer Science Computer Science, Artificial Intelligence Computer Science, Interdisciplinary Applications Dynamic propagation Fake news detection Multi-view learning RUMOR DETECTION Science & Technology Technology Time decay

A Multi-View Framework for Fake News Detection Utilizing Dynamic User Propagation Structures, Temporal Changes, and Personal Attributes

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Open access

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