## 1 Introduction
That was a bold step from a PolitiFact-designated \"parody\" fake news site, and it serves as yet another example of how the line between satire and intentional misinformation is complicated ([[https://www.poynter.org/fact-checking/2017/a-satirical-fake-news-site-apologized-for-making-a-story-too-real/](https://www.poynter.org/fact-checking/2017/a-satirical-fake-news-site-apologized-for-making-a-story-too-real/)]([https://www.poynter.org/fact-checking/2017/a-satirical-fake-news-site-apologized-for-making-a-story-too-real/](https://www.poynter.org/fact-checking/2017/a-satirical-fake-news-site-apologized-for-making-a-story-too-real/)) (accessed on 1 September 2023)). Satirical articles are a common form of commentary, but when written in a journalistic format, they can pose a unique danger to readers. Since some satirical articles are written to appear as fact, readers may be fooled into believing that the information contained in the article is true. This can lead to confusion and misinformation, as well as sectariism and even hate speech if the satire is based on polarizing issues. Furthermore, it can be difficult for readers to distinguish between a serious article and another that is satirical, as subtle jokes and puns may go unnoticed if one is not familiar with the subject matter. As such, it is important for readers to be aware of the potential for satirical articles to be misleading, and to exercise caution when engaging with them. To aid with the aforementioned issue, we aim to develop a model that automatically classifies Arabic satirical articles. Additionally, we investigate the impact of several feature extraction methods on classifying Arabic satirical articles. To the best of our knowledge, this work is the first to present a thorough analysis in terms of Arabic satirical content written in a formal journalistic register. The main contributions of this study are:  
* Compile a large Arabic news satirist articles dataset;
* Perform a thorough analysis of the impact of several traditional and innovative feature extraction methods for satirical articles classification;
* Build a satire classification model using machine learning (ML), deep learning(DL), and transformers;
* Perform a detailed linguistic analysis of the formation of satirical articles;
* Create an open-source satire classification platform. The experiments ran on a dataset of satirical and non-satirical articles that were collected from real-world satirical and non-satirical news platforms. Data are found in GitHub ([[https://github.com/Noza12347](https://github.com/Noza12347) tab=projects]([https://github.com/Noza12347](https://github.com/Noza12347) tab=projects) (accessed on 1 September 2023)), and can be made available on reasonable request. Our research contributes to the field of Arabic natural language processing by shedding light on the compilation of Arabic satire classification models using various feature extraction techniques integrated with machine learning, deep learning, and transformers. The remainder of the paper is organized as follows. Section 2 gives the background for satirical news, then Section 3 presents related work in the field of satirical news detection. Section 4 describes the dataset, preprocessing operations, and the classification models. Finally, the design of the experiments along with their results is described in Section 5.