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
33rd IEEE Conference on Signal Processing and Communications Applications, SIU 2025, İstanbul, Türkiye, 25 - 28 Haziran 2025, (Tam Metin Bildiri)
- Yayın Türü: Bildiri / Tam Metin Bildiri
- Doi Numarası: 10.1109/siu66497.2025.11112123
- Basıldığı Şehir: İstanbul
- Basıldığı Ülke: Türkiye
- Anahtar Kelimeler: BERTurk, convolutional neural networks, fake news detection, support vector machines, text classification, Turkish fact-checking
- Orta Doğu Teknik Üniversitesi Adresli: Evet
Özet
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.