## 4 Experimental Setup and Results
### Main Results Analysis  
The COVID-19 pandemic has highlighted the importance of social media as a critical platform for information dissemination, including news, fake news, propaganda, and even self-reported symptoms. In this study, we meticulously analyzed public discussions related to COVID-19 on Facebook and Twitter, compiling the content into a unified repository named ProText. The primary objective was to discern various propaganda types within these public discussions on social media platforms, specifically focusing on the COVID-19 theme. In addition, our research involved conducting multi-label multi-class experiments using models such as RoBERTa and BERT, and their variants. These experiments were compared with results obtained from traditional algorithms. DL techniques were applied individually, incorporating a specific set of features to construct a self-learning corpus. \begin{table}
\begin{tabular}{l l l l l l} \hline \hline \multirow{2}{*}{**Hyper-Parameters**} & \multicolumn{2}{l}{**Detail**} & \multirow{2}{*}{**Hyper-Parameters**} & \multicolumn{2}{l}{**Detail**} \\ \cline{2-2} \cline{4-6}  & **Min** & & **Max** & **Min** & **Max** \\ \hline Optimizer & Adam & Adam & Learning Rates & 2 \(\times\) 10\({}^{-3}\) & 5 \(\times\) 10\({}^{-3}\) \\ Output Layer & Softmax & Softmax & Epochs & 10 & 15 \\ Hidden Layers & 768 \(\times\) 100 & 768 \(\times\) 200 & Pre-train model layer & 12 & 24 \\ Data Train Size & 0.6 & 0.8 & _n_clusters_ & 1 & 2 \\ Data Test Size & 0.4 & 0.2 & Weight decay & 0.1 & 0.0001 \\ Dropout & 0.1 & 0.3 & Batch sizes & 16 & 32 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: All hyper-parameters tuning range and its detail values with all datasets. The study's analysis also included comparing optimization methods to identify the optimal parameters for benchmark algorithms. Remarkably, our ML-MC classification model outperformed existing state-of-the-art techniques, achieving an impressive accuracy rate of 88%. The insights gained from this research are invaluable, especially for government and healthcare agencies. These findings can be used to effectively detect and monitor propaganda on social media platforms, enhancing their ability to combat misinformation during the ongoing COVID-19 crisis. The BERT model utilized the pre-trained \"bert-base-uncased\" version, comprising 110 million parameters trained on lower-cased English text [21]. During prediction, label distributions in all reached leaves were aggregated for each test document. RoBERTa, BERT, MT-DNN [54], XLNET [56], and ALBERT [55] models were enhanced with NLP features, integrating LSTM and CRF networks [6; 35]. These models were compared based on propaganda techniques using the ProText Dev, training, and test datasets, as illustrated in Figure 8.  
The proposed ML-MC approach utilizing RoBERTa with CRF and LSTM effectively tackled substantial challenges and consistently achieved F1 scores of around 80% or higher. Notably, other methods faced memory constraints, highlighting the scalability and efficiency of the RoBERTa-based approach. The ML-MC-RoBERTa-CRF model was meticulously fine-tuned and achieved commendable performance metrics, including an F1 score of 0.7861, a precision of 0.7992, and a recall of 0.7734 when evaluated on the ProText dataset. In parallel, the ML-MC-RoBERTa-LSTM model demonstrated even higher proficiency, securing an impressive F1 score of 0.8363, along with a precision of 0.8319, and a recall of 0.8275. These results highlight the effectiveness of the proposed propaganda detection models, which exhibit substantial accuracy in identifying propaganda classes within each theme, as shown in Figure 9.  
We also conducted robustness tests by introducing various categories and modifying hyper-parameters to assess the model's resilience. Our proposed model demonstrated superior performance when evaluated against conventional propaganda themes. This outcome underscores the value of NLP features in propaganda prediction, particularly when employing ML-MC techniques on the ProText dataset.