# Enhancing Fake News Detection in Romanian Using Transformer-Based Back Translation Augmentation
## 4 Results and Discussion
\begin{table}
\begin{tabular}{l l c c c c c c} \hline \hline
**Augmentation Type** & **Model Name** & **Acc** & **Pre** & **Rec** & **F1** & **AUC** & **Kappa** \\ \hline \multirow{4}{*}{None} & Extra Trees Classifier & 0.7277 & 0.8034 & 0.7287 & 0.7301 & 0.8211 & 0.4438 \\  & Random Forest Classifier & **0.7371** & 0.8017 & **0.7519** & **0.7389** & **0.8128** & **0.4586** \\  & Support Vector Machine & 0.6948 & **0.8137** & 0.6434 & 0.6981 & 0.7743 & 0.3950 \\  & Logistic Regression & 0.7042 & 0.7845 & 0.7054 & 0.7070 & 0.7586 & 0.3971 \\ \hline \multirow{4}{*}{Back Translation (Google Translate)} & Extra Trees Classifier & 0.7746 & 0.8189 & 0.8062 & 0.7751 & 0.8434 & 0.5302 \\  & Random Forest Classifier & **0.7793** & 0.8306 & **0.7984** & **0.7804** & **0.8510** & **0.5428** \\ \cline{1-1}  & Support Vector Machine & 0.7559 & **0.8407** & 0.7364 & 0.7584 & 0.8158 & 0.5054 \\ \cline{1-1}  & Logistic Regression & 0.7324 & 0.8000 & 0.7442 & 0.7344 & 0.7712 & 0.4501 \\ \hline \multirow{4}{*}{Back Translation (mBART)} & Extra Trees Classifier & 0.7840 & 0.8430 & 0.7907 & 0.7855 & 0.8449 & 0.5553 \\ \cline{1-1}  & Random Forest Classifier & **0.8075** & **0.8548** & **0.8217** & **0.8084** & **0.8451** & **0.6012** \\ \cline{1-1}  & Support Vector Machine & 0.7465 & 0.8319 & 0.7287 & 0.7491 & 0.8157 & 0.4863 \\ \cline{1-1}  & Logistic Regression & 0.7089 & 0.7863 & 0.7132 & 0.7115 & 0.7723 & 0.4054 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Scores for fake news detection with and without augmentation (Google Translate and mBART) using French as the target language. with the Support Vector Machine performing the lowest and the Random Forest Classifier performing the highest. The precision, recall, and F1 score also varied among the models, with the Random Forest Classifier having the highest recall and F1 score and the Support Logistic Regression having the highest precision. The precision scores for these models were relatively high, with values between 0.7845 and 0.8137, indicating that the models could correctly identify a large proportion of fake news instances. However, the recall scores for these models were relatively low, suggesting that they had difficulty correctly identifying all the fake instances. When the BT technique was used with Google Translate and English as the target language (Table 4), there was a notable improvement in the scores for all classifiers. The Extra Trees Classifier and Random Forest Classifier achieved high scores across all metrics, with an accuracy of 0.7746 and 0.7793 respectively. Furthermore, the Support Vector Machine model showed impressive enhancement in precision and recall scores, with an 8.12% and 12.05% increase compared to models without data augmentation. Similar results were observed for models trained on augmented data using the mBART model for BT and the English language as target. The best performing classifier was Random Forest, with an accuracy of 0.7746, a recall of 0.7907, an F1 score of 0.7759, and an AUC of 0.8462. The Extra Trees Classifier performed remarkably well, achieving an accuracy rate of 0.7700 and an F1 score of 0.7716, closely following the top-performing algorithm. In Table 5, the results of the second experiment showed a significant improvement in the model performance when using transformer-based BT techniques, particularly with French as the target language. For example, the accuracy of the Random Forest Classifier increased from 0.7371 to 0.7793 when Google Translate BT was used and increased to 0.8075 when mBART BT was used. The most effective model here was the Random Forest Classifier augmented with BT via mBART, offering the highest values of accuracy (0.8075), precision (0.8548), recall (0.8217), F1 score (0.8084), and AUC (0.8451) as well as achieving a Kappa score of 0.6012. We can also observe in Figure 2 that the use of Google Translate for BT resulted in better performance compared to the original data set, but not as significant as mBART.