## 6 Conclusions and Further Work  
The approach outlined in this paper underscores the significance of Transformer models in identifying political fake news, particularly within the Spanish language, a domain that has received limited attention in current research. We contribute by creating a high-quality and well-balanced dataset, bridging a gap in publicly available datasets for Spanish fake news detection. This collection is regarded as a valuable resource for future research, simplifying the construction and evaluation of models in this specific domain, and addressing the challenge of countering misinformation in a widely spoken language that is currently underrepresented in public datasets. The superior performance of our fine-tuned model compared to the original models can be attributed to several key improvements, particularly those associated with the RoBERTa and BERT models. RoBERTa introduces several optimizations over BERT, mainly focusing on training efficiency rather than changing the underlying architecture. These include training on a significantly larger dataset and for more steps. RoBERTa replaces BERT's static masking with dynamic masking, where the masked words change in each training iteration. It also removes the Next Sentence Prediction (NSP) task, which was found to add little value. Additionally, RoBERTa uses larger batch sizes, trains with longer sequences from the beginning, and fine-tunes hyperparameters more extensively. These improvements allow the model to be exposed to more data and enhance the adjustment of weights, ultimately enabling RoBERTa to capture the complexities of natural language more effectively, leading to better performance in tasks like fake news detection, as our results revealed. Our approach also benefits from using multilingual models BERT and RoBERTa, as well as Spanish-specific models such as BETO, RoBERTa-BERTIN, and RoBERTa-BNE, which are crucial for handling the nuances of the Spanish language, an especially important aspect for detecting fake news in Spanish political contexts. Additionally, fine-tuning the models on political news from Spain provides them with domain-specific knowledge, enabling better differentiation between true and false information in this specialized area. Even though generating synthetic fake news has potential risks, several important considerations have guided our decision to incorporate this approach into our research. Although there are challenges, such as token distribution differences between real and synthetic data, synthetic data offer significant advantages in contexts like Spanish political news, where direct sources for extracting fake news are limited. The scarcity of datasets specifically for Spanish-language fake news led us to explore alternative methods for generating diverse training data. To ensure the credibility of our data, we sourced authentic news articles from reputable, independent outlets known for their impartiality and balanced reporting, which is crucial in the political domain. Regarding linguistic and stylistic consistency, our dataset is composed of formal news articles from established journalistic sources in Spain, where differences in language use across regions are minimal. This formal consistency between real and synthetic data minimizes concerns about regional variations affecting the model's performance. For the generation of synthetic fake news, we addressed potential risks by modifying only specific factual details, such as names, dates, or figures, while maintaining the original structure, style, and syntax of the real news. This ensures that the tokens remain largely consistent with real data, making it difficult for the model to distinguish between natural and synthetic news based on superficial linguistic patterns, thus enabling the model to focus on detecting misinformation patterns aligned with the goals of our study.