BERT and SVM Integration for Fake News Detection in Turkish: Evaluation with a New Dataset T rk e Sahte Haber Tespiti i in BERT ve SVM Entegrasyonu: Yeni Bir Veri K mesi ile De?gerlendirme

Date

2025-01-01

Author

Cural, Necla Mutlu
Karabulut, Cemile
Karagöz, Pınar

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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This study presents an expanded new dataset and a hybrid BERT-SVM model for fake news detection in Turkish. As part of the research, news articles collected from FCTR, SOSYALAN, X-Fact datasets and Turkish fact-checking platforms teyit.org, and dogrulukpayi.com were combined to create a comprehensive dataset containing more than 20 thousand news articles. To improve classification accuracy, a hybrid approach integrating a fine-tuned BERTurk model with Support Vector Machines (SVM) was proposed. Additionally, model predictions were evaluated in terms of stylistic bias. The results demonstrate that BERT-based hybrid models have significant potential in addressing the unique challenges of fake news detection in Turkish.

Subject Keywords

BERTurk, convolutional neural networks, fake news detection, support vector machines, text classification, Turkish fact-checking

URI

https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=105015402273&origin=inward
https://hdl.handle.net/11511/115846

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Department of Computer Engineering, Conference / Seminar