# Text Data Augmentation Techniques for Fake News Detection in the Romanian Language
## 5 Results and Discussion
\begin{table}
\begin{tabular}{l l c c c c c} \hline \hline
**Augmentation Type** & **Model Name** & **Acc** & **Pre** & **Rec** & **F1** & **AUC** & **Kappa** \\ \hline \multirow{4}{*}{None *} & Extra Trees Classifier & **0.7418** & **0.7174** & 0.7500 & **0.7421** & 0.8013 & **0.4836** \\  & Random Forest Classifier & 0.7043 & 0.6602 & 0.7727 & 0.7037 & **0.8043** & 0.4120 \\  & Support Vector Machine & 0.7204 & 0.6698 & **0.8068** & 0.7193 & 0.7822 & 0.4450 \\  & Logistic Regression & 0.6667 & 0.6182 & 0.7727 & 0.6641 & 0.7401 & 0.3398 \\ \hline \multirow{4}{*}{Back Translation} & Extra Trees Classifier & **0.8065** & **0.8269** & 0.8269 & **0.8065** & **0.8586** & **0.6074** \\  & Random Forest Classifier & 0.7957 & 0.8000 & **0.8462** & 0.7947 & 0.8513 & 0.5824 \\  & Support Vector Machine & 0.7581 & 0.7706 & 0.8077 & 0.7571 & 0.8433 & 0.5061 \\  & Logistic Regression & 0.7527 & 0.7685 & 0.7981 & 0.7519 & 0.8252 & 0.4958 \\ \hline \multirow{4}{*}{Easy Data Augmentation} & Extra Trees Classifier & **0.7742** & **0.7981** & **0.7981** & **0.7742** & **0.8566** & **0.5420** \\  & Random Forest Classifier & 0.7581 & 0.7810 & 0.7885 & 0.7579 & 0.8405 & 0.5086 \\ \cline{1-1}  & Support Vector Machine & 0.7473 & 0.7615 & **0.7981** & 0.7463 & 0.8444 & 0.4841 \\ \cline{1-1}  & Logistic Regression & 0.7419 & 0.7545 & **0.7981** & 0.7407 & 0.8231 & 0.4725 \\ \hline \hline \end{tabular}  
* Original data set extracted from www.factual.ro (accessed on 17 June 2023).  
\end{table} 
Table 6: Scores for models without (none) and with data augmentation (BT and EDA) on content-based data sets. \begin{table}
\begin{tabular}{l l c c c c c} \hline \hline
**Augmentation Type** & **Model Name** & **Acc** & **Pre** & **Rec** & **F1** & **AUC** & **Kappa** \\ \hline \multirow{4}{*}{None *} & Extra Trees Classifier & **0.7204** & **0.6765** & 0.7841 & **0.7200** & 0.8214 & 0.4438 \\  & Random Forest Classifier & 0.6828 & 0.6218 & **0.8409** & 0.6767 & 0.8058 & 0.3750 \\  & Support Vector Machine & **0.7204** & 0.6698 & 0.8068 & 0.7193 & **0.8255** & **0.4450** \\  & Logistic Regression & 0.6613 & 0.6106 & 0.7841 & 0.6576 & 0.7828 & 0.3303 \\ \hline \multirow{4}{*}{Back Translation} & Extra Trees Classifier & 0.7903 & 0.7982 & 0.8365 & 0.7895 & **0.8773** & 0.5719 \\  & Random Forest Classifier & **0.8118** & 0.8053 & **0.8750** & **0.8103** & 0.8687 & **0.6138** \\  & Support Vector Machine & 0.7796 & **0.8058** & 0.7981 & 0.7797 & 0.8538 & 0.5535 \\ \hline \multirow{4}{*}{Easy Data Augmentation} & Extra Trees Classifier & **0.7796** & 0.7739 & **0.8558** & **0.7772** & **0.8581** & **0.5464** \\  & Random Forest Classifier & 0.7742 & 0.7672 & **0.8558** & 0.7714 & 0.8535 & 0.5348 \\ \cline{1-1}  & Support Vector Machine & 0.7527 & 0.7736 & 0.7885 & 0.7523 & 0.8485 & 0.4971 \\ \cline{1-1}  & Logistic Regression & 0.7742 & **0.7768** & 0.8365 & 0.7726 & 0.8186 & 0.5372 \\ \hline \hline \end{tabular}  
* Original data set extracted from www.factual.ro (accessed on 17 June 2023).  
\end{table} 
Table 7: Scores for models without (none) and with data augmentation (BT and EDA) on context-based data sets. In general, enhancements can have a significant impact on the performance of fake news detection models in the Romanian language. BT augmentation achieved the highest overall performance across all models. On the other hand, if we consider models that were trained on data without augmentation, the Extra Trees Classifier achieved the highest performance of all models, while the Logistic Regression model achieved the lowest performance. Analysis of AUC scores for classification models used to detect fake news in the Factual.ro data set has shown that data augmentation (BT and EDA) has led to an increase in AUC scores for all models in both content-based and context-based data sets (Figure 4). Especially notable is the improvement in the Extra Trees Classifier AUC score in context-based data sets, which increased from 0.8214 to 0.8773.