## 3 Crafting a Spanish Fake News Dataset
### Developing a Custom Dataset for Spanish Politics-Related Fake News  
As shown in the brief excerpt from our Spanish Political Fake News dataset depicted in Figure 1, this corpus includes the following fields for each news article:  
* ID: A unique identifier for each news item.
* Label: A classification label designating each news item as either fake (0) or real (1).
* Headline (_Titulo_ in Figure 1): The headline of the news article.
* Description (_Descripicon_ in Figure 1): A more detailed description of the news article, providing additional insights into the subject matter, personalities, or events mentioned within.
* Date (Fecha in Figure 1): The publication date of the news articles in DD/MM/YYYY format, commonly adopted in Spanish.  
The process of constructing our dataset of fake news in Spanish involves a combination of different techniques, which we will explore throughout this section.  
* Firstly, we employed web scraping to automatically extract real news from information published between April 2017 and June 2023. These data were gathered from the websites of reputable, independent media outlets that have established a strong reputation for reliability and impartiality. These outlets are known for avoiding political bias in their coverage of a wide range of topics, specifically those related to national politics. This process allowed us to collect authentic news articles that provide a balanced and accurate reflection of political events, which is critical for ensuring the credibility of the dataset (further details can be found in Section 3.2.1).
* Secondly, as explained in Section 3.2.2, we leveraged public APIs provided by the National Institute of Statistics of Spain to generate a diverse set of fact-based news articles. These APIs gave access to a vast array of objective statistical data on a variety of politically relevant matters, such as economic performance, unemployment rates, healthcare metrics, and more. By using this wealth of data, we were able to produce news content that reflected real-world political and socio-economic trends, ensuring  
Figure 1: Excerpt from our dataset of news articles related to politics in the Spanish language, which includes a total of 57,231 entries, comprising 52% real news and 48% fake news.  
that our dataset maintained accuracy and relevance in terms of the key political issues that shape public discourse.
* Finally, due to the lack of publicly available repositories of political fake news in Spanish, we employed a hybrid approach to generating misleading content. This approach involved two distinct methods. The first method utilized the generative capabilities of AI, particularly a Generative Pre-trained Transformer (GPT) model, to automatically create a multitude of varied headlines. By using the OpenAI library, we generated fake headlines that closely mimicked the style and structure of real news articles (Section 3.2.3). The second method involved making strategic alterations to real news articles to create misleading information. These alterations typically included changing the names of political leaders or parties, as well as modifying key figures such as unemployment rates or dates, thereby creating plausible but false representations of political events. More detailed explanations of these methods are provided in Section 3.2.4.