An Efficient Fusion Network for Fake News Classification

Muhammad Swaileh A. Alzaidi, Alya Alshammari, Abdulkhaleq Q. A. Hassan, Samia Nawaz Yousafzai, Adel Thaljaoui (), Norma Latif Fitriyani, Changgyun Kim () and Muhammad Syafrudin ()
Additional contact information
Muhammad Swaileh A. Alzaidi: Department of English Language, College of Language Sciences, King Saud University, P.O. Box 145111, Riyadh 11421, Saudi Arabia
Alya Alshammari: Department of Applied Linguistics, College of Languages, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia
Abdulkhaleq Q. A. Hassan: Department of English, College of Science and Arts at Mahayil, King Khalid University, Abha 62529, Saudi Arabia
Samia Nawaz Yousafzai: Applied INTelligence Lab (AINTLab), Seoul 05006, Republic of Korea
Adel Thaljaoui: Department of Computer Science and Information, College of Science at Zulfi, Majmaah University, P.O. Box 66, Al-Majmaah 11952, Saudi Arabia
Norma Latif Fitriyani: Department of Artificial Intelligence and Data Science, Sejong University, Seoul 05006, Republic of Korea
Changgyun Kim: Department of Artificial Intelligence & Software, Kangwon National University, Samcheok 25913, Republic of Korea
Muhammad Syafrudin: Department of Artificial Intelligence and Data Science, Sejong University, Seoul 05006, Republic of Korea

Mathematics, 2024, vol. 12, issue 20, 1-20

Abstract: Nowadays, it is very tough to differentiate between real news and fake news due to fast-growing social networks and technological progress. Manipulative news is defined as calculated misinformation with the aim of creating false beliefs. This kind of fake news is highly detrimental to society since it deepens political division and weakens trust in authorities and institutions. Therefore, the identification of fake news has emerged as a major field of research that seeks to validate content. The proposed model operates in two stages: First, TF - IDF is applied to an entire document to obtain its global features, and its spatial and temporal features are simultaneously obtained by employing Bidirectional Encoder Representations from Transformers and Bidirectional Long Short-Term Memory with a Gated Recurrent Unit. The Fast Learning Network efficiently classifies the extracted features. Comparative experiments were conducted on three easily and publicly obtainable large-scale datasets for the purposes of analyzing the efficiency of the approach proposed. The results also show how well the model performs compared with past methods of classification.

Keywords: fake news classification; word embedding; TF - IDF; BERT; self-attention; BiLSTM-GRU; fast learning network (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.mdpi.com/2227-7390/12/20/3294/pdf (application/pdf)
https://www.mdpi.com/2227-7390/12/20/3294/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jmathe:v:12:y:2024:i:20:p:3294-:d:1502847

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().