# Enhancing Fake News Detection in Romanian Using Transformer-Based Back Translation Augmentation
## 4 Results and Discussion
The Extra Trees Classifier and the Random Forest Classifier performed the best, with accuracy scores of 0.7746 and 0.7793, respectively. Support Vector Machine and Logistic Regression models also showed improvements, with the latter achieving an accuracy score of 0.7324. Comparing the performance of the models on the initial data set (without augmentation) and the back-translated data sets, we can see an improvement in all metrics (accuracy, precision, recall, F1 score, AUC, and [PERSON]'s kappa score) when using transformer-based models to augment the data through BT. The results demonstrate the effectiveness of BT augmentation in improving performance for text classification tasks. This finding is consistent with previous research [36; 37], which demonstrated the efficacy of utilizing pre-trained models like mBART for BT augmentation. By leveraging the pre-trained language modeling capabilities of mBART, we can generate high-quality synthetic translations that can be used to augment the training data. Using pre-trained models like mBART in BT augmentation implementations offers several advantages. First, these models have been trained in large-scale data sets and have learned significant semantic knowledge from text [38]. This enables them to generate high-quality back-translated sentences that preserve the meaning and context of the initial text. Second, pre-trained models provide a transferable knowledge base that can be leveraged for different tasks and languages [39]. Furthermore, comparing the performance of the models using Google Translate and mBART for BT, we can see that using mBART with French as the target language results in slightly better scores for most models, with higher accuracy, precision, F1 score, and [PERSON]'s kappa score. This suggests that mBART is a more effective augmentation method for Romanian fake news detection when using a language closer to the target language. More research can be undertaken to compare the effectiveness of these methods in other languages and their impact on detecting fake news in Romanian. The results also show that classifiers such as Extra Trees and Random Forest were the best performing models for both Google Translate and mBART. In contrast, the Support Vector Machine and Logistic Regression models performed poorly. This could be because tree-based models are generally more effective in handling non-linear relationships and noise in the data [29; 30], which is often the case in fake news detection. The methodology employed in this research can improve model performance in languages with limited resources. Nevertheless, there are certain obstacles to consider. First, the pre-trained models used for BT augmentation must be trained in the language in question. For example, the mBART model employed in this study was trained in 50 languages. Second, some target languages may be better suited for the scenario. Several target languages can be employed to generate augmented data sets for model training, and the language that results in the most significant improvements in model performance can be chosen. Figure 2: Comparison of accuracy, precision, and recall for models without BT (None) and with BT (Google Translate and mBART): (**a**) English as target language; (**b**) French as target language.