## 3 Materials and Methods
### Dataset
#### 3.1.1 Arabic Documents Texts Datasets
As shown in Table 2, Arabic text documents in this dataset are categorized into seven different classes (i.e., Middle East news, world news, business and economy, sports, international press, science and technology, and art and culture). In total, this dataset contains 1,860,786 (1.8 M) Arabic words and 106,733 district keywords after removing the stop words. CNN Arabic Corpus  
This dataset was collected from the CNN website, [[https://arabic.cnn.com](https://arabic.cnn.com)]([https://arabic.cnn.com](https://arabic.cnn.com)) (accessed on 15 July 2021) and included 5070 Arabic text documents [52]. As shortlisted in Table 2, each text document is categorized into six different classes (i.e., Middle East news, world news, business and economy, sports, science and technology, and entertainment). In total, this dataset contains 2,241,348 (2.2 M) Arabic words and 144,460 distinct keywords after stop word removal. This dataset is publically available at [[http://site.iugaza.edu.ps/msaad/osac-open-source-arabic-corpora](http://site.iugaza.edu.ps/msaad/osac-open-source-arabic-corpora)]([http://site.iugaza.edu.ps/msaad/osac-open-source-arabic-corpora](http://site.iugaza.edu.ps/msaad/osac-open-source-arabic-corpora)) (accessed on 15 July 2021) [52]. Open Source Arabic Corpus (OSAC)  
This dataset was collected from multiple sources and websites to include 22,429 Arabic text documents [52]. As shown in Table 3, each text document is categorized into ten different classes (i.e., economy, history, education and family, religion, sports, health, astronomy, low, stories, and cooking recipes). This dataset contains 18,183,511 (18 M) Arabic words and 449,600 distinct keywords after removing the stop words. We can find this dataset at [[http://site.iugaza.edu.ps/msaad/osac-open-source-arabic-corpora](http://site.iugaza.edu.ps/msaad/osac-open-source-arabic-corpora)]([http://site.iugaza.edu.ps/msaad/osac-open-source-arabic-corpora](http://site.iugaza.edu.ps/msaad/osac-open-source-arabic-corpora)) (accessed on 15 July 2021) [52]. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**\#** & **Class Type** & **BBC** & **CNN** \\ \hline
1 & Middle East News & 2356 & 1462 \\
2 & World News & 1489 & 1010 \\
3 & Business and Economy & 296 & 836 \\
4 & Sports & 219 & 762 \\
5 & International Press & 49 & - \\
6 & Science and Technology & 232 & 526 \\
7 & Entertainments & - & 474 \\
8 & Art and Culture & 122 & - \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Data Distribution per Class for BBC and CNN Arabic Corpuses. Arabic Influencer Twitter Dataset (AITD)  
This dataset was generated by collecting different twists for 60 Arab influucers on Twitter [53]. Domain experts categorized this dataset into ten different classes, as shown in Table 4. A Twitter application programming interface (API) was used to collect the last 3200 tweets for each account. This dataset was labeled based on the aspect of the Twitter user profile. If the user profile belongs to the health category, all related tweets for that user are categorized to be in the health class. This dataset is available at this link [[https://github.com/shammur/Arabic_news_text_classification_datasets](https://github.com/shammur/Arabic_news_text_classification_datasets)]([https://github.com/shammur/Arabic_news_text_classification_datasets](https://github.com/shammur/Arabic_news_text_classification_datasets)) (accessed on 15 July 2021) [53].