Comparative Analysis of Machine Learning, Deep Learning, and Transformer-Based Models for Fake News Detection

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dc.contributor.authorGürer, Aybuke
dc.contributor.authorSert, Eser
dc.date.accessioned2026-06-19T06:31:56Z
dc.date.available2026-06-19T06:31:56Z
dc.date.issued2025
dc.departmentMalatya Turgut Özal Üniversitesi
dc.description9th International Artificial Intelligence and Data Processing Symposium, IDAP 2025 -- 6 September 2025 through 7 September 2025 -- Malatya -- 215321
dc.description.abstractThe widespread of fake news on digital and social media has alarming consequences for public perception, political stability, and social confidence. This study provides a comparative evalution of various machine learning, deep learning, and transformer-based models. The more than 25,000 labeled news articles in the publicly accessible ISOT Fake and Real News Dataset served as the basis for all tests. Eight different models, such as Support Vector Machine (SVM), Convolutional Neural Network (CNN), and BERT, were created by splitting the data into 80% training and 20% test groups. Feature extraction was performed and trained using TF-IDF feature vectors for traditional models, text sequential representations for deep learning models, and contextual embeddings for transformer models. Eight different models, such as Support Vector Machine (SVM), Convolutional Neural Network (CNN), and BERT, were created by splitting the data into 80% training and 20% test groups. The CNN model (99.84% accuracy and 1.0000 ROC-AUC) achieved the highest accuracy and the best ROC-AUC scores. The CNN model is followed by BERT (99.65% accuracy), which has a complex architecture. However, simpler models such as Logistic Regression and SVM also yielded surprising results with accuracy exceeding 99%. These results suggest the importance of data quality and feature representation for fake news detection. © 2025 IEEE.
dc.identifier.doi10.1109/IDAP68205.2025.11222166
dc.identifier.isbn979-833158990-5
dc.identifier.scopus2-s2.0-105025015420
dc.identifier.scopusqualityN/A
dc.identifier.urihttps://doi.org/10.1109/IDAP68205.2025.11222166
dc.identifier.urihttps://hdl.handle.net/20.500.12899/4881
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherInstitute of Electrical and Electronics Engineers Inc.
dc.relation.ispartof9th International Artificial Intelligence and Data Processing Symposium, IDAP 2025
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_Scopus_20260612
dc.subjectDeep Learning
dc.subjectFake News
dc.subjectMachine Learning
dc.subjectTransformer Based Model
dc.titleComparative Analysis of Machine Learning, Deep Learning, and Transformer-Based Models for Fake News Detection
dc.typeConference Object

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