## 3 Crafting a Spanish Fake News Dataset
### Known Limitations and Desired Features  
The need to establish a Spanish-language dataset dedicated to political disinformation arises from the inherent constraints identified in current datasets, as outlined in Section 2. These limitations have impeded the development of robust and accurate classification models, which are essential for combating disinformation within the Spanish-language political context. The identified constraints involve the following features:  
* Size of the datasets. Current Spanish datasets are notably small, with a total number of news articles tagged as 'fake' or'real' that falls below 3000 [12; 13; 14]. This lack of data compromises the ability of classification models to effectively learn to distinguish between true and false news.
* Topic of the news. These datasets encompass a broad spectrum of topics extending beyond the Spanish political context, making it challenging to build models specialized in the detection of political fake news.
* Format errors in news. Errors have been observed in the news articles, such as missing fields and incorrect categorizations, affecting the quality of the data. Additionally, current fake news datasets often lack temporal references, potentially leading to inaccurate and biased results in classification models since such references are indispensable for evaluating the credibility of news content. Significantly, data such as the current president's name or statistics regarding unemployment and economic growth are inherently time sensitive. It is imperative that news articles within these datasets incorporate temporal markers to guarantee the seamless functioning of fake news detection models, ultimately improving the accuracy of misinformation identification.  
To overcome these limitations, this paper describes the creation of a customized dataset with the following requirements:  
* Our dataset should contain a significantly larger volume of news articles to address the issue of small size.
* Furthermore, the new corpus should collect genuine news from reputable and trustworthy sources. In the absence of reliable equivalent sources for fake news, a procedure for generating fake news in a balanced manner becomes necessary. This should ensure a comprehensive and equitable dataset that reflects both real and fake information, thereby enhancing the model's ability to accurately distinguish between them and, consequently, address misinformation effectively.
* The new dataset should include news articles of varying lengths, allowing the model to process headlines of different extensions without compromising its effectiveness, while also addressing issues related to formatting errors in the news articles.
* Regarding thematic diversity, the news articles in the new dataset should cover a variety of topics within the political context, such as economy, society, culture, among others. This will enable the model to verify information considering the vocabulary used in the articles, thus addressing the issue of excessively broad subject matter in current datasets.  
Finally, it is crucial to ensure that the new dataset contains both subtle and significant differences between real and fake news articles. This is relevant because, as the similarity in language and structure between real and fake news intensifies, the task of differentiation becomes more challenging for the model. Therefore, the dataset creation process should meticulously highlight these nuanced distinctions. This approach empowers the model to more effectively identify even the most subtle variations between accurate information and misleading content, thereby enhancing its proficiency in combating misinformation.