## 2 Related Work
The former leverages the structural relationships in graph data to analyze connections and patterns within the social network, aiding in identifying misleading information [29]. Meanwhile, attention networks focus on important parts of the input data by assigning different levels of importance, allowing the model to prioritize relevant features, better detect nuances indicative of fake news, and even manage handle multimodal misinformation detection in multimedia content [30]. Most studies have predominantly concentrated on English-language news, showcasing notable outcomes with high accuracy rates within the range of 84% to 92% [3; 31; 32; 33]. In [12], detection is performed on a relatively small dataset containing 2000 news articles in Spanish, equally divided between real and fake news, which was created in [34]. As depicted in Table 1, the obtained accuracy values are as follows: 64.25% with XGBoost, 77% with logistic regression, and 74.25% with Random Forest. In [14], various classic ML methods are explored such as DT, SVM, NB and K-Nearest Neighbors (KNN), for classifying Mexican Spanish news as either 'fake' or 'true'. This experiment employs a news corpus comprising pairs of real and fake Spanish-language publications collected between November 2020 and March 2021 from newspaper websites, media companies, and fact-checking platforms. The dataset consists of 971 instances, with balanced representation of fake and true news in the test dataset [35]. Experimental results reveal that SVM and KNN are the most effective classification models in this context, achieving an accuracy of 81%. In [13] an approach to detect fake news in Spanish, specifically in tweets posted during the 2022 Colombbian presidential elections, is described. This work compares the performance of ML and DL techniques by pitting RF against a fine-tuned BERT model. In both cases, a relatively small corpus comprising just over 600 Spanish tweets related to the 2022 elections, equally divided between fake and real news, was used, obtaining an accuracy of 89.90% with RF and 95.80% with BERT. The superior performance of Transformer models in text classification tasks has been confirmed in various research studies using BERT models and variants for fake news detection in quite different languages, such as English [23], Greek [44], and Bengali [45]. These studies achieved accuracy values of 83%, 93%, and 92%, respectively. The authors of [33] proposed MVPBert, which utilizes two parallel BERT networks to perform veracity detection in English using the Fakenewsnet dataset containing approximately 23,000 news items [41]. Fakenewsnet is a significant dataset consisting of full-length news articles obtained from [[https://www.politifact.com](https://www.politifact.com)]([https://www.politifact.com](https://www.politifact.com)) (accessed on 1 November 2023). In contrast to other fake news datasets that often comprise shorter sources like tweets and headlines, Fakenewsnet provides extensive news articles. In this work, one BERT network encodes news headlines, while the other encodes the news body. Due to the constant and limited input length of the BERT network and the typically longer news body, the authors employ the MaxWorth algorithm to select the most valuable part of the news text for fact checking, achieving an accuracy value of 85.4%. The approach outlined in [42] employs a BETO model, a dedicated architecture trained from scratch with documents in Spanish. In particular, this model, designed as the Spanish counterpart of BERT (originally developed for English), has undergone fine-tuning using a corpus containing 2500 Spanish news articles. The achieved accuracy rate exceeds 80% [34; 43]. The revision of existing approaches made throughout this section reveals that Transformer models, notably BERT and its variants, outperform traditional ML methods for fake news detection, achieving higher accuracy rates and robustness across various languages and contexts.