DeepFND: an ensemble-based deep learning approach for the optimization and improvement of fake news detection in digital platform

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dc.authoridSimic, Vladimir/0000-0001-5709-3744
dc.authoridTirkolaee, Erfan Babaee/0000-0003-1664-9210
dc.authoridPham, Duc T/0000-0003-3148-2404
dc.authorwosidSimic, Vladimir/B-8837-2011
dc.authorwosidTirkolaee, Erfan Babaee/U-3676-2017
dc.authorwosidPham, Duc T/H-1516-2011
dc.contributor.authorVenkatachalam, K.
dc.contributor.authorAl-onazi, Badriyya B.
dc.contributor.authorSimic, Vladimir
dc.contributor.authorTirkolaee, Erfan Babaee
dc.contributor.authorJana, Chiranjibe
dc.date.accessioned2024-05-19T14:41:26Z
dc.date.available2024-05-19T14:41:26Z
dc.date.issued2023
dc.departmentİstinye Üniversitesien_US
dc.description.abstractEarly identification of false news is now essential to save lives from the dangers posed by its spread. People keep sharing false information even after it has been debunked. Those responsible for spreading misleading information in the first place should face the consequences, not the victims of their actions. Understanding how misinformation travels and how to stop it is an absolute need for society and government. Consequently, the necessity to identify false news from genuine stories has emerged with the rise of these social media platforms. One of the tough issues of conventional methodologies is identifying false news. In recent years, neural network models' performance has surpassed that of classic machine learning approaches because of their superior feature extraction. This research presents Deep learningbased Fake News Detection (DeepFND). This technique has Visual Geometry Group 19 (VGG-19) and Bidirectional Long Short Term Memory (Bi-LSTM) ensemble models for identifying misinformation spread through social media. This system uses an ensemble deep learning (DL) strategy to extract characteristics from the article's text and photos. The joint feature extractor and the attention modules are used with an ensemble approach, including pre-training and fine-tuning phases. In this article, we utilized a unique customized loss function. In this research, we look at methods for detecting bogus news on the internet without human intervention. We used the Weibo, liar, PHEME, fake and real news, and Buzzfeed datasets to analyze fake and real news. Multiple methods for identifying fake news are compared and contrasted. Precision procedures have been used to calculate the proposed model's output. The model's 99.88% accuracy is better than expected.en_US
dc.description.sponsorshipPrincess Nourah bint Abdulrahman University [PNURSP2022R263]; Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabiaen_US
dc.description.sponsorshipPrincess Nourah bint Abdulrahman University Researchers Supporting Project number (PNURSP2022R263) , Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia supported the APC for this article. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.en_US
dc.identifier.doi10.7717/peerj-cs.1666
dc.identifier.issn2376-5992
dc.identifier.pmid38192452en_US
dc.identifier.scopus2-s2.0-85182979167en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.urihttps://doi.org10.7717/peerj-cs.1666
dc.identifier.urihttps://hdl.handle.net/20.500.12713/5109
dc.identifier.volume9en_US
dc.identifier.wosWOS:001120971000001en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherPeerj Incen_US
dc.relation.ispartofPeerj Computer Scienceen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.snmz20240519_kaen_US
dc.subjectFake Newsen_US
dc.subjectDeepfnden_US
dc.subjectDeep Learningen_US
dc.subjectEnsemble Modelen_US
dc.subjectJoint Feature Extractionen_US
dc.titleDeepFND: an ensemble-based deep learning approach for the optimization and improvement of fake news detection in digital platformen_US
dc.typeArticleen_US

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