# Romanian Fake News Detection Using Machine Learning and Transformer-Based Approaches
## 5 Discussion  
In this section we point out most important finds of the comparative analysis of machine learning algorithms and Transformer-based models on Romanian fake news detection using three different datasets for training and tests: FakeRom, NEW, and both FakeRom + NEW. The BERT model trained on the NEW dataset consistently achieved the highest performance metrics across all test sets. When evaluated on the combined test set from both FakeRom and NEW, BERT reached an accuracy of 96.5%, with a precision of 96.7% and recall of 96.5%. This model also exhibited an excellent ability to differentiate between the various classes, as reflected in its high ROC AUC score of 0.997. These results suggest that BERT trained on the NEW dataset is highly effective at generalizing across diverse sources of fake news, misinformation, and real news, highlighting its robustness and adaptability to different data characteristics. The RoBERTa-large model trained on NEW was another top performer, reaching an accuracy of 94.4% on the combined test set. Similar to BERT, RoBERTa-large exhibited strong metrics across precision (94.6%), recall (94.4%), and F1 score (94.4%). Its ROC AUC scores for each class were also very high, with near-perfect scores, particularly for the satire class, where it reached an AUC of 1.0. The model's performance on both the FakeRom and NEW datasets suggests it can effectively generalize to unseen data, making it a powerful tool for identifying fake news across different sources. The SVM model trained on the NEW dataset also demonstrated competitive performance, achieving an accuracy of 94.6% on the combined test set. The model exhibited strong precision (94.8%) and recall (94.6%), with an F1 score of 94.6%. The ROC AUC scores were consistently high, particularly for classes like satire and real news, where it achieved scores close to 1.0. SVM's ability to perform well on both the NEW and FakeRom datasets highlights its robustness, although it slightly lagged behind the Transformer-based models BERT and RoBERTa-large. The models trained on the NEW dataset outperformed their counterparts trained on FakeRom, particularly when tested on mixed or cross-dataset scenarios. BERT and RoBERTa-large stand out as the best-performing models overall, showcasing superior accuracy, precision, recall, and F1 scores across multiple test sets. These results suggest that Transformer-based models are highly effective for fake news detection tasks, particularly when trained on diverse and representative datasets like the NEW dataset. SVM, while not as advanced as BERT or RoBERTa-large, remains a strong contender due to its consistency across different test sets, though it generally performs best when combined with the larger and more diverse NEW dataset. These findings indicate that the choice of dataset plays a critical role in the performance of machine learning models. Models trained on the NEW dataset not only perform better on this dataset but also exhibit strong generalization to other datasets, such as FakeRom. This underlines the importance of using rich and diverse datasets to train models for fake news detection, ensuring they are well-equipped to handle real-world variability in misinformation and news content. The evaluation results provide clear evidence that the quality and diversity of the dataset significantly influence the performance of machine learning models for fake news detection. Models trained on the NEW dataset consistently outperformed those trained on the FakeRom dataset across a variety of test sets. This was especially evident when the models were evaluated on cross-dataset scenarios or combined test sets, where modelstrained on FakeRom showed a significant performance decline, while those trained on the NEW dataset maintained high accuracy and other performance metrics. One of the key takeaways is that the NEW dataset offers greater diversity and robustness, allowing models to generalize more effectively to unseen or mixed data.