## 3 Material and Methods
### Data Collection
#### 3.1.1 Data Pre-Processing  
The prevalence of self-established or poorly reviewed news articles on social media often results in a lack of editorial oversight and fact-checking [46], leading to the introduction of typos and dialects that add noise to the data. This underscores the importance of optimizing pre-processing steps for effective propaganda detection. Given the noisy nature of social media text, this stage employs a series of operations including tokenization, data augmentation, stemming, cleaning, and stop-word removal to create structured and reliable inputs for the model. Furthermore, we employed a TF-IDF weighting scheme to identify the model's features, representing each instance by a vector of term weights. The pre-processing phase encompassed several procedures (see Figure 3) to enhance classification accuracy, including text language and domain identification, dimension reduction, and more [47]. **Step 1**: Tokenization. Tokenization, or text segmentation, divides the text into tokens using delimiters such as commas, spaces, and periods to create distinct units for analysis. **Step 2**: Data augmentation and lowercase. During our experiments on ProText datasets, we encountered an imbalance between non-propaganda and propaganda news data. To address this, we performed data augmentation to increase the size of the propaganda news dataset. This involved four operations: random insertion (RI), random deletion (RD), synonymous substitution (SS), and random exchange (RE) [48]. Table 3 presents examples of augmented sentences. The implementation details and parameter configurations are as follows: \begin{table}
\begin{tabular}{l l l l l l l l} \hline \hline \multirow{2}{*}{**Dataset**} & \multicolumn{3}{c}{**Split Instance Process**} & \multirow{2}{*}{**Test**} & \multirow{2}{*}{**Total Data**} & \multirow{2}{*}{**Class**} & \multirow{2}{*}{**Theme**} \\  & & **Train** & & & & & \\ \hline \multirow{2}{*}{ProText} & Facebook & 5142 & 734 & 1469 & 7345 & 6 & 6 \\  & Twitter & 3588 & 512 & 1025 & 5125 & 6 & 6 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Propaganda contextual instances of datasets in a variety of circumstances. Figure 2: The attachment of popular Tweets/Facebook posts between 2020 and 2023. * Random Insertion (RI): A synonym of a randomly selected word in the sentence is inserted at a random position. For each sentence, up to two insertions were performed (insertion rate = 0.2). * Random Deletion (RD): Each word in the sentence was randomly deleted with a probability of 0.1 (deletion rate = 0.1). * Synonym Substitution (SS): A maximum of two words per sentence were replaced with their synonyms using WordNet-based similarity matching, ensuring semantic coherence (substitution rate = 0.2). * Random Swap (RS): Two randomly selected words in a sentence were swapped. This was repeated up to two times per sentence (swap rate = 0.2). **Step 3**: Stemming. Using NLTK, stemming was performed to reduce words to their root forms, which minimized morphological variations, reduced feature space, and enhanced classification performance [32,49]. **Step 4**: Data cleaning. The text data were cleaned by removing numbers, non-English letters, special characters (#, 5, %, %, &), URL tags, punctuation marks, and other irrelevant data. **Step 5**: Stop-word removal. Stop-words, which are common and insignificant words such as prepositions, conjunctions, and pronouns, were removed to improve classification performance. Figure 3: Sample instances before and after pre-processing.