Yeni Medya ve Dezenformasyon: Makine Öğrenmesi ile Sahte Haberlerin Tespitine İletişimsel Bir Yaklaşım

Yıl 2025, Cilt: 25 Sayı: 4, 246 - 267, 29.12.2025

İsnur İnci Armutlu , Şahide Şeker

https://doi.org/10.18037/ausbd.1650095

Öz

Yeni medya, bilgiye hızlı erişim ve geniş kitlelere anında ulaşım imkânı sunarak iletişim alanında köklü değişimlere yol açmaktadır. Ancak bu dönüşüm, aynı zamanda dezenformasyonun yayılmasını kolaylaştırarak sahte haberlerin artmasına neden olmuştur. Sahte haberlerin tespiti için geliştirilen yöntemler arasında, makine öğrenmesi algoritmaları öne çıkmaktadır. Bu çalışmada, sahte haber tespitinde makine öğrenmesi tekniklerinin etkinliği incelenmiştir. Çalışmada, Kaggle platformunda yer alan "Fake and Real News Dataset" veri seti kullanılarak lojistik regresyon modeli ile bir sınıflandırma gerçekleştirilmiştir. Haber metinleri, TF-IDF yöntemiyle vektörleştirilmiş ve modelin eğitimi bu özellikler üzerinden yapılmıştır. Modelin başarısı doğruluk, kesinlik, duyarlılık ve F1-skoru gibi değerlendirme metrikleriyle ölçülmüştür. Elde edilen bulgular, lojistik regresyonun sahte haberleri tespit etmede yüksek performans gösterdiğini ortaya koymaktadır. Çalışma, dijital medya ekosisteminde sahte haberlerin tespitinde veri odaklı yaklaşımların önemini vurgulamakta ve medya okuryazarlığının artırılmasına yönelik öneriler sunmaktadır.

Anahtar Kelimeler

yeni medya , iletişim , dezenformasyon , makine öğrenmesi , lojistik regresyon modeli

New Media and Disinformation: A Communicative Approach to Detecting Fake News Using Machine Learning

Öz

New media has brought fundamental changes to the field of communication by providing rapid access to information and enabling instant reach to large audiences. However, this transformation has also facilitated the spread of disinformation, leading to an increase in fake news. Among the methods developed for detecting fake news, machine learning algorithms stand out. This study examines the effectiveness of machine learning techniques in detecting fake news. A classification was performed using a logistic regression model on the Fake and Real News Dataset available on the Kaggle platform. The news texts were vectorized using the TF-IDF method, and the model was trained based on these features. The performance of the model was evaluated using accuracy, precision, recall, and F1-score metrics. The findings indicate that logistic regression demonstrates high performance in detecting fake news. This study highlights the importance of data-driven approaches in identifying fake news within the digital media ecosystem and offers recommendations for enhancing media literacy.

Anahtar Kelimeler

new media , communication , disinformation , machine learning , logistic regression model

Kaynakça

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