## 5 Results and Experimental Discussion
### Analyzing Test Metrics of the Fine-Tuned Models  
The models analyzed in our study are evaluated using five well-known metrics: accuracy, precision, F1 score, recall, and MCC. We present the results achieved by the models without fine-tuning in Table 5, and those corresponding to their fine-tuned versions on our domain-specific dataset in Table 6.  
The results depicted in Table 5 show that the pre-trained models, without fine-tuning, exhibit poor performance, with metrics suggesting behavior close to random, as indicated by MCC values near zero. This can be explained by the fact that base models are generally trained on extensive but generalized corpora. While this gives them a broad understanding of language, they lack the specialized knowledge required for our binary classification task. In our case, the dataset provides domain-specific information, particularly in distinguishing between fake and real news in the context of national politics. Without fine-tuning, the models are unable to grasp the nuanced patterns needed for accurate classification, as shown by the experimental results. On the contrary, the values of Table 6 corroborate the notable capabilities of all fine-tuned variants, with indicators exceeding 0.9, and also reaffirm the superior performance of _RoBERTa-BNE_ compared to its counterparts. In the context of fake news detection, the model's accuracy score of 0.986 signifies its ability to accurately classify over 98% of news articles as either genuine or fake. Precision, F1 and recall scores, ranging between 0.985 and 0.991 highlight the model's proficiency in not only reducing false positives but also effectively identifying a significant proportion of actual fake news instances. Furthermore, the MCC value of 0.971 underscores the model's robustness, as it can make precise predictions while taking into account both false positives and false negatives. This establishes the _RoBERTa-BNE_ model as an certainly reliable choice for fake news detection, particularly in the domain of politics and in Spanish, two areas that have been relatively underexplored in the state-of-the-art research. The metrics also reveal that the _RoBERTa-BERTIN_ model outperforms _BETO_, although the latter exhibits slightly better performance on previously unseen data compared to the multilingual versions of BERT and RoBERTa. The results obtained in the previous sections confirm that our fine-tuned Transformer-based models achieve a very notable performance in identifying fake news in the dataset of Spanish political news we have collected. However, since this dataset combines natural true news items with synthetically generated fake ones, it is possible that the fine-tuned models have learned to distinguish between the two types of data rather than learning to detect fake news, which is the goal of our research. This distinction might arise from subtle \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Fine-Tuned Model** & **Test Accuracy** & **Test Precision** & **Test F-1 Score** & **Test Recall** & **Test MCC** \\ \hline _BERT-multilingual_ & 0.925 & 0.922 & 0.930 & 0.938 & 0.911 \\ _BETO_ & 0.927 & 0.927 & 0.931 & 0.935 & 0.913 \\ \hline _RoBERTa-multilingual_ & 0.924 & 0.921 & 0.928 & 0.935 & 0.912 \\ _RoBERTa-BERTIN_ & 0.931 & 0.930 & 0.934 & 0.940 & 0.928 \\ _RoBERTa-BNE_ & **0.986** & **0.985** & **0.988** & **0.991** & **0.971** \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Metrics assessed during testing of the fine-tuned BERT-based and RoBERTa-based models using previously unseen data, which constitute 20% of our Spanish political fake news dataset.