# Enhancing Fake News Detection in Romanian Using Transformer-Based Back Translation Augmentation
## 3 Methodology
### Evaluation  
Different metrics were used to evaluate the performance of our models, including accuracy, precision, recall, F1 score, area under the curve, and Cohen's kappa. Evaluating a classifier's performance involves presenting actual and predicted classification information in a confusion matrix. This matrix provides a tabular representation of the classifier's performance, as shown in Table 3.  
In detecting fake news, True Positives (TP) refer to instances correctly identified as fake, and False Negatives (FN) refer to fake instances incorrectly classified as real. False Positives (FP) are real instances that are incorrectly classified as fake, and True Negatives (TN) are instances that are correctly classified as real. Based on these elements, we can define the metrics mentioned above as follows:  
* Precision is the percentage of fake news instances correctly predicted out of all those predicted as fake. \[\text{Precision}=\frac{\text{TP}}{\text{TP}+\text{FP}}\] (1)
* Recall is the percentage of instances of false news correctly predicted out of all instances of actual false news. \[\text{Recall}=\frac{\text{TP}}{\text{TP}+\text{FN}}\] (2)
* Accuracy is the measure of the general correctness of the predictions of the model. It calculates the ratio of correctly predicted instances (fake and non-fake news) to the total number of instances. \[\text{Accuracy}=\frac{\text{TP}+\text{TN}}{\text{TP}+\text{TN}+\text{FP}+ \text{FN}}\times 100\] (3)
* The F1 score provides a balanced assessment of model performance, combining accuracy and recall. \[\text{F1}=\frac{(2\times\text{Precision}\times\text{Recall})}{\text{ Precision}+\text{Recall}}\] (4)
* The area under the curve is a common method to measure the accuracy of a binary classifier system, for example, for the detection of fake news. It provides a single metric that summarizes the classifier's performance over all possible thresholds. A high AUC value indicates the model's capability to differentiate between fake and real news accurately.
* [PERSON]'s kappa score, also known as Kappa, is a statistical measure that assesses the agreement between two classifiers in identifying if a news instance is fake or not. It is calculated by comparing the observed level of agreement between the two classifiers (P\({}_{\text{o}}\)) with the level of agreement expected by chance (P\({}_{\text{e}}\)) [35]. \[\text{Kappa}=\frac{\text{P}_{\text{o}}-\text{P}_{\text{e}}}{1-\text{P}_{ \text{e}}}\] (5)  
We used k-fold cross-validation for the original data set, with k set to 10, to provide additional reliability in the model evaluation process. This technique is beneficial in assessing the model's performance across different subsets of the data. In the 10-fold cross-validation for the augmented data, we employed stratified sampling to maintain  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & Predicted Positive & Predicted Negative \\ \hline Actual Positive & True Positive (TP) & False Negative (FN) \\ Actual Negative & False Positive (FP) & True Negative (TN) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Confusion matrix.  
the balance between the classes across folds, given that the fake news instances may vary in distribution.