## 5 Results and Experimental Discussion
### Analyzing Test Metrics of the Fine-Tuned Models
#### 5.2.2 Performance Benchmark with Related Approaches  
To conclude our experimental validation, we present a comparison between our leading model, _RoBERTa-BNE_, and various models discussed in Section 2. In this benchmarking, we have considered the two following criteria when selecting recent relevant approaches: those employing Transformers, whether for fake news detection specifically or for general text classification across multiple languages; and works that specifically focus on fake news detection in Spanish, incorporating both Transformer-based and traditional ML approaches. It is important to note that our models and those referenced in the literature for benchmarking were not evaluated on the same datasets for two primary reasons. First, although most datasets used in the literature are accessible through popular repositories, the corresponding model codes are often unpublished, preventing direct comparison with our fine-tuned Transformers. Second, a fair comparison would require evaluating our fake news detection approach in Spanish, focused on politics, against similar approaches in the same language and thematic area. However, most published works concentrate on generic news in English and do not specifically address the political topic. Therefore, in line with our previous comments, we have compiled results from relevant studies utilizing Transformers to provide a comprehensive view of the state of the art in fake news detection and to highlight our contributions to a relatively unexplored area: detecting political fake news in Spanish. The benchmarking results can be seen in Table 7.  
In essence, our model fine-tuned on the synthetic dataset has delivered highly promising results, showcasing a remarkable accuracy rate of 98.6%. Moreover, the other fine-tuned models on our dataset have also produced outstanding outcomes, with the lowest accuracy reaching 92.5%. Significantly, this metric not only evidences a considerable performance enhancement compared to approaches employing various ML techniques (rows 1 and 2 in Table 7) but also stands out in contrast to works utilizing Transformer models. Our \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Approach** & **Adopted Technique** & **Dataset** & **Size** & **Language** & **Accuracy** \\ \hline \multirow{2}{*}{[12]} & Random Forest & & & & 74.25\% \\  & XGBoost & Fakes1000 [34] & 2000 & Spanish & 64.25\% \\  & Logistic Regression & & & & 77\% \\ \hline \multirow{4}{*}{[14]} & SVM & & & 81\% \\  & Naive Bayes & & & 59\% \\  & KNN (K = 3) & The Spanish Fake News corpus [35] & 971 & Mexican Spanish & 81\% \\  & Decision Tree & & & & 65\% \\ \hline \multirow{2}{*}{[13]} & Random Forest & & & & 89.80\% \\  & BERT & Unpublished dataset & 634 & Spanish & 95.80\% \\ \hline \multirow{2}{*}{[33]} & Two parallel BERT networks & Fakenewsnet [41] & 23,000 & English & 85.4\% \\ \hline \multirow{2}{*}{[42]} & BETO & Corpus extracted from [34; 43] & 2500 & Spanish & 80\% \\ \hline \multirow{2}{*}{[15]} & BERT & EXIST [36] & 13,000 & English & 76.83\% \\ \hline \multirow{4}{*}{[16]} & \multirow{4}{*}{BERT} & IMDB movie reviews [37] & 50,000 & English & 93.87\% \\  & & RealOrNot tweets [38] & 10,000 & English & 83.64\% \\ \cline{1-1}  & & Portuguese news [39] & 12,145 & Portuguese & 91.20\% \\ \cline{1-1}  & & Chinese hotel reviews [40] & 6000 & Chinese & 93.81\% \\ \hline \multirow{2}{*}{Our approach} & A fine-tuned & The model has been fine-tuned and & & \\  & RoBERTa-BNE model. & tested on our Spanish Politician Fake & 57,231 & Spanish & **98.6\%** \\ \cline{1-1}  & & News synthetic dataset. & & & \\ \hline \multirow{2}{*}{Our approach} & The same fine-tuned & The model was fine-tuned on the & & & \\  & & synthetic dataset and later tested on a & & \\ \cline{1-1}  & RoBERTa-BNE model as in the & new non-synthetic fake news dataset, & 63 & Spanish & **71\%** \\ \cline{1-1}  & previous row. & including political news from Spain’s & & & \\ \cline{1-1}  & & July 2023 electoral debates. & & & \\ \hline \hline \end{tabular}
\end{table}