Romanian Fake News Detection using Machine Learning and Transformer-Based Approaches

Nowadays, the consequence of quick access to information has lead to the spread of fake news, which has a strong damaging impact on democracy, justice, and public trust. Thus, it is crucial to analyze and evaluate detection methods of fake news. This paper focuses on the detection of Romanian fake news. In this study, we made a comparative analysis of machine learning algorithms and transformer-based models on Romanian fake news detection using three datasets – FakeRom, New, and both FakeRom + New. The NEW dataset was build using a scrapping algorithm applied on the Veridica platform. Our approach uses for detection machine learning models: Native Bayes (NB,) Logistic regression (LR) and Support Vector Machine (SVM). We also used two transformer based models – BERT based multilingual based and RoBERTaLarge. The performance of the models was evaluated using various metrics: accuracy, precision, recall and F1-score. The results revealed that BERT model trained on NEW dataset consistently achieved the highest performance metrics across all test sets. Also, Support Vector Machine trained on NEW was another top performer, reaching a very good accuracy on the combined test set.