## 6 Conclusions and Further Work
In addition to customizing models for a specific topic and language, we perform a comparative analysis to assess the effectiveness of our fake news detection approach compared to other relevant works. Our experiments confirm that the _RoBERTa-BNE_ model achieves exceptional precision, exceeding 98% when fine-tuned on our dataset. This surpasses previous methods that relied on combining multiple BERT models with lower-quality datasets across different languages, including Spanish. However, testing with a much smaller independent dataset of manually curated real and non-synthetic fake news reveals a significant drop in performance to 71%. This decline underscores the need for further adjustments to effectively address real-world challenges in accurately detecting variations within authentic fake news data. Nonetheless, this result seems promising as an initial exploration into a less-explored research domain such as political news detection in Spanish. In addition to these refinements, our future research will focus on several key areas. First, we plan to explore ways to leverage user feedback to enhance the model's accuracy and usability. To gather this feedback effectively, we aim to implement a system that encourages users to share their experiences. One approach is to create user surveys that capture insights about their interactions with the fake news detection model, particularly regarding prediction accuracy and overall usability. Additionally, introducing a'report error' button will enable users to directly flag misclassifications, simplifying the feedback process. By analyzing this input, we can identify patterns in misclassifications and make targeted improvements. Second, we intend to investigate a multimodal approach to enhance political fake news detection in Spanish by integrating text and images, especially in social media contexts. By analyzing visual elements, such as infographics and misleading images, alongside textual narratives, we can gain deeper insights into misinformation dissemination. This integration will reveal inconsistencies that text analysis alone may miss. Leveraging convolutional neural networks (CNNs) for image analysis, combined with Transformer-based models discussed in this paper, can help identify patterns indicative of fake news. Finally, we see potential in examining user engagement metrics on social media, such as shares and comments, to assess the virality of political content. Analyzing how users interact with news articles can provide valuable insights into which political messages resonate with audiences and why they are shared. Conceptualization, [PERSON] and [PERSON]; methodology, [PERSON]-F. and [PERSON]-V.; software, [PERSON]V. and [PERSON]-[PERSON]; validation, [PERSON]-V. and [PERSON]; formal analysis, [PERSON]-F. and [PERSON]-[PERSON]; investigation, [PERSON]-F., [PERSON]-V. and [PERSON]-[PERSON]; data curation, [PERSON]-V. and [PERSON]; writing--original draft preparation, [PERSON]-F. and [PERSON]; writing--review and editing, [PERSON]-F., [PERSON] and [PERSON]; supervision, [PERSON] and [PERSON]; project administration, [PERSON]. All authors have read and agreed to the published version of the manuscript. **Funding:** This project has received support from the Ministry of Science and Innovation of the Government of Spain within the framework of the DEBATrue research project (PDC2021-121720-100), as well as from the grants for the consolidation and structuring of competitive research units awarded by the Conselleria de Educacion, Ciencia, Universidades y Formacion Profesional of the Xunta de Galicia (reference ED431B 2024/26). **Institutional Review Board Statement:** Not applicable. **Informed Consent Statement:** Not applicable.