## 7 Conclusions and Future Work  
Arabic satire classification has become an influential topic with the rise of social media. We conducted a comprehensive empirical study to compile the Arabic satrical articles classification model using several feature extraction methods that included n-grams, textual features, and word embedding models with several ML, DL, and transformers. While there have been satire classification projects that investigated textual content found in social media, our study is the first that focuses on Arabic satrical articles written in a formal journalistic manner. To dispel any biased content, we collected the satrical articles from real-world satrical news platforms that explicitly follow a journalistic format. Though the dataset used in this study is relatively moderate, several findings were well-founded. We found that BERT reached an F1-score of 95%, outperforming other traditional feature extraction methods with ML and DL algorithms. On the other hand, traditional methods such as n-grams, specifically unigrams, combined with CNN reached high F1-scores of 94.1%. This indicates the validity of the use of traditional methods closely performing with new methods such as transformers. Additionally, the use of unigrams outperformed bigrams and trigrams, which we provided a justification for such insights. We also investigated and detailed the use of ArabicGPt in the classification of Arabic satrical articles, and the challenge of detecting implicit satrical content was reported. An in-depth lexical analysis that supports the understanding of satrical content, which may help researchers build models that solve the detection of implicit satire, was reported. Finally, a fully developed model is provided freely to classify unorganized files into satrical and non-satrical textual files. Our study broadens up the tasks of Arabic satire classification, which include satrical content written in a journalistic format, and offers methods for building models that differentiate satrical articles from non-satrical articles. This work can be integrated with social media platforms to guide the reader to satrical content, which can fight against the distribution of false information. For future work, we will alter the values of certain factors when constructing the models, and investigate their performance. It is also possible to combine the probability scores of several deep learning architectures with conventional classifiers to increase performance. We also plan to set forward a larger and more varied dataset to investigate the performance of several hybrid deep learning models and compare them to the results from ChatGPT. Conceptualization, F.A. and H.H. ; methodology, F.A. and H.H. ; software, H.H. ; validation, F.A, and H.H. ; analysis, F.A. ; investigation, H.H. ; resources, F.A and H.H. ; data curation, H.H. ; writing--original draft preparation, F.A. ; writing--review and editing, H.H. ; visual  
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