Scrutnet: a Deep Ensemble Network for Detecting Fake News in Online Text

dc.authorscopusid 57274634600
dc.authorscopusid 59702178300
dc.authorscopusid 57208839546
dc.authorscopusid 55450561600
dc.authorscopusid 57205606652
dc.authorscopusid 25824675400
dc.authorscopusid 25824675400
dc.contributor.author Verma, Aryan
dc.contributor.author Priyanka, P.
dc.contributor.author Khan, Tayyab
dc.contributor.author Singh, Karan
dc.contributor.author Yesufu, Lawal . O.
dc.contributor.author Ariffin, Mazeyanti Mohd
dc.contributor.author Ahmadian, Ali
dc.date.accessioned 2025-04-15T23:53:21Z
dc.date.available 2025-04-15T23:53:21Z
dc.date.issued 2025
dc.department Okan University en_US
dc.department-temp [Verma, Aryan] Univ Edinburgh, Sch Math, Edinburgh EH8 9YL, Midlothian, Scotland; [Priyanka, P.] Natl Inst Technol Hamirpur, Dept Comp Sci & Engn, Hamirpur 177005, Himachal Prades, India; [Khan, Tayyab] Jawaharlal Nehru Univ, Sch Comp & Syst Sci, New Delhi 110067, India; [Singh, Karan] IIIT Sonepat, Dept Comp Sci & Engn, Sonipat 131001, India; [Yesufu, Lawal . O.] Hult Int Business Sch, Dubai, U Arab Emirates; [Ariffin, Mazeyanti Mohd] Univ Teknol PETRONAS, Posit Comp Res Cluster, Seri Iskandar 32610, Perak, Malaysia; [Ahmadian, Ali] Istanbul Okan Univ, Fac Engn & Nat Sci, Istanbul, Turkiye; [Verma, Aryan; Ahmadian, Ali] Jadara Univ, Jadara Res Ctr, Irbid 21110, Jordan en_US
dc.description.abstract The expeditious propagation of fake news through online social media platforms has cropped up as a captious challenge, undermining the credibility of information sources and affecting public trust. Accurate detection of fake news is imperative to maintain the integrity of online content but is constrained by availability of data. This research aims to detect fake news from online articles by proposing a novel deep learning ensemble network capable of effectively discerning between genuine and fabricated news articles using limited data. We introduce ScrutNet, which leverages the synergistic capabilities of a bidirectional long short-term memory network and a convolutional neural network, which have been meticulously designed and fine-tuned for the task by us. This comprehensive ensemble classifier captures both sequential dependencies and local patterns within the textual data without requiring very large datasets like transformer based models. Through rigorous experimentation, we optimise the individual model parameters and ensemble strategy. The experimental results showcase the remarkable efficacy of ScrutNet in the detection of fake news, with an outstanding precision of 99.56%, 99.43% specificity, and an F1 score of 99.49% achieved on the partition test of the data set. Comparative analysis against state-of-the-art baselines demonstrates the superior performance of ScrutNet, establishing its prominence as a generalised and dependable fake news detection mechanism. en_US
dc.description.woscitationindex Emerging Sources Citation Index
dc.identifier.doi 10.1007/s13278-025-01412-3
dc.identifier.issn 1869-5450
dc.identifier.issn 1869-5469
dc.identifier.issue 1 en_US
dc.identifier.scopus 2-s2.0-105000463545
dc.identifier.scopusquality Q2
dc.identifier.uri https://doi.org/10.1007/s13278-025-01412-3
dc.identifier.uri https://hdl.handle.net/20.500.14517/7786
dc.identifier.volume 15 en_US
dc.identifier.wos WOS:001449047800004
dc.identifier.wosquality N/A
dc.language.iso en en_US
dc.publisher Springer Wien en_US
dc.relation.publicationcategory Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı en_US
dc.rights info:eu-repo/semantics/openAccess en_US
dc.subject Fake News Detection en_US
dc.subject Bi-Directional Lstm en_US
dc.subject Cnn en_US
dc.subject Social Media en_US
dc.title Scrutnet: a Deep Ensemble Network for Detecting Fake News in Online Text en_US
dc.type Article en_US

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