## 3 Related Work  
Classification of Arabic text, especially fake news, has gained much attention in recent years. In the context of satirical text classification, ML and DL models have been investigated with a variety of features, such as textual and contextual features. Recently, pretrained models, such as BERT, were used in this field and gave promising results. Figure 1: Pretrained transformer model architecture. Satirical news classification can be implemented by examining the textual content, to differentiate between satirical and non-satirical content. For that, a classifier was trained on a dataset containing satirical Arabic tweets to detect satirical news was built [28]. It focused on surface features, sentiment features, shifter features, and contextual features. Features were grouped based on prior studies written in non-English languages, and as a result, this classifier reached an accuracy of 72.36%, despite the difficulty of processing Arabic social media texts and the absence of tools to deal with translating words in lexicons. Some studies have applied classification techniques to detect fake news. A broader perspective that includes Arabic news articles has been adopted [37]. An exploratory analysis was conducted to identify the linguistic properties of satirical Arabic content as a type of fake news. The researchers analyzed satirical articles by searching for specific features: journalistic register (terminology commonly used in journalistic writing), sentiment intensity measures, and subjectivity measures (by analyzing pronouns that denote first-person inflections). [PERSON] et al. claimed that these lexical features were sufficient to classify satirical Arabic fake news accurately [37]; however, the nature of satirical articles is quite different from news articles. Machine learning, and more recently deep learning algorithms, have been used to detect satirical text [19; 38; 39; 40]. A systematic review study conducted by [PERSON] et al. [27] using Twitter data found 31 related studies. Algorithms were classified into Adapted Machine learning algorithms (AMLA), such as support vector machine (SVM) and logistic regression (LR), and other customized machine learning algorithms (CMLA), such as a semi-supervised satiire identification algorithm and bootstrapping. The study found that AMILA algorithms were widely used in satire detection studies, and SVM was the most efficient one to predict satire on Twitter. Additionally, lexical, pragmatic, frequency, and part-of-speech tagging features improved the performance of these algorithms. Arabic satire detection has its complications; [PERSON] and [PERSON] found that the Naive Bayes model using lexical and structural features outperformed random forest and logistic regression with an accuracy of 89.17% for Arabic tweets [21]. Ensemble classifiers were developed to further improve the detection of satirical content [41]; the combination of random forest, logistic regression, and decision tree provided the best performance. To further improve the detection, BERT was combined with contextual features [42]. The presented approach improved the detection significantly compared to the state of the art. For social media data that are short texts, a hybrid autoencoder-based model, which consists of bidirectional encoder representations from the transformer (BERT), universal sentence encoder (USE), and an unsupervised learning long-short-term-memory-based autoencoder, were proposed [43]. The accuracy reached 92% on one of the used datasets. Other versions of BERT models were used for Arabic satire detection [24; 25; 34; 44], showing promising results. A recent study that investigated the automation of satire detection from 2010 to 2022 [45] found that deep learning algorithms, such as convolutional neural networks and long short-term memory, performed better than machine learning algorithms. [PERSON] et al. developed an Arabic dataset from Twitters [46]. They applied ML and DL algorithms to study their impact on detecting satirical articles.