## 3 Material and Methods  
To address the challenges of detecting propaganda in COVID-19-related social media discussions, we developed an innovative ML-MC classification approach, structured into three primary phases. These phases focus on the novel aspects of our methodology, emphasizing the contributions of our proposed model while minimizing the discussion of existing methods.  
The _first_ phase involves data collection, pre-processing, and annotation. We curated a comprehensive dataset from public discussions on social media platforms, including Facebook and Twitter. This dataset was manually annotated to ensure high-quality training data for our model. Pre-processing involved removing irrelevant content, such as common stopwords, and applying targeted filters to retain information-rich text regions. This step also included labeling specific propaganda techniques and COVID-19-related themes, as outlined in our proposed taxonomy. The meticulous annotation process, guided by domain experts in NLP, ensured robust and reliable input for subsequent analysis.  
In the _second_ phase, we focused on feature representation and DL-based text classification, leveraging advanced techniques to extract meaningful insights from the data. Instead of relying solely on traditional methods such as Term Frequency-Inverse Document Frequency (TF-IDF), we employed modern embedding techniques that integrate contextual word relationships. These embeddings were generated using state-of-the-art pre-trainedmodels such as RoBERTa and BERT, which capture nuanced linguistic patterns essential for accurate propaganda detection. Our approach further incorporated task-specific tuning to enhance the effectiveness of these embeddings in addressing the unique challenges of multi-label classification.  
The _third_ phase emphasized the novel ML-MC classification framework and propaganda detection. We developed a multi-layered architecture that integrates embedding generation, task-specific classification layers, and a final softmax-based classifier. This framework was tailored to detect and classify intricate propaganda techniques at the sentence level. Our approach uniquely combines supervised learning for structured labeling with unsupervised learning to capture latent patterns in the data, enhancing both interpretability and predictive performance. The inclusion of advanced models such as RoBERTa and ALBERT, alongside domain-specific optimizations, underscores the originality and rigor of our methodology.  
Figure 1 illustrates the complete propaganda detection pipeline, detailing each stage from data collection and pre-processing to classification and final integration. This pipeline represents a cohesive and systematic approach, showcasing the interplay between novel algorithmic designs, advanced embeddings, and robust classification strategies. By focusing on these three phases, our methodology demonstrates its innovative contributions to propaganda detection, emphasizing the integration of cutting-edge DL models, sophisticated data processing, and a well-structured classification framework. This approach directly addresses the limitations of existing methods, offering a significant advancement in the field.