## 5 Discussion
### Summary of the Findings  
_Optimization and performance:_ The analyses identified optimal parameters for benchmark algorithms, demonstrating that our ML-MC classification model outperformed state  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Sen-Length**} & \multicolumn{4}{c}{**Twitter**} & \multicolumn{4}{c}{**Sen-Length**} & \multicolumn{4}{c}{**Facebook**} \\ \cline{2-10}  & **F1** & **Pre** & **Rec** & **Acc** & & **F1** & **Pre** & **Rec** & **Acc** \\ \hline
12 & 0.3374 & 0.3280 & 0.3474 & 38.52\% & 80 & 0.4823 & 0.4924 & 0.4726 & 50.87\% \\
30 & 0.4460 & 0.4391 & 0.4531 & 49.97\% & 140 & 0.5989 & 0.6049 & 0.5930 & 64.75\% \\
90 & 0.5427 & 0.5602 & 0.5319 & 57.43\% & 200 & 0.7037 & 0.6943 & 0.7134 & 74.08\% \\
120 & 0.4273 & 0.4074 & 0.4171 & 40.61\% & 320 & 0.6481 & 0.6503 & 0.6459 & 69.30\% \\
60 & **0.7179** & 0.6868 & 0.7020 & **76.08\%** & 260 & **0.8185** & 0.8143 & 0.  
of-the-art methods with an impressive 88% accuracy rate. In the context of the diffusion of innovations theory [10], this result highlights the rapid adoption and effectiveness of our advanced models. The model's accuracy underscores its potential to help government and healthcare agencies in effectively combating misinformation and accelerating the diffusion of accurate information. _Feature extraction_: The findings indicate the consistent vocabulary across datasets reinforces the importance of reliable features in detecting propaganda. Through the lens of diffusion of innovations theory, maintaining a consistent vocabulary and stable feature set facilitates quicker and more efficient identification and dissemination of truthful information--but also is critical for the accuracy and effectiveness of NLP and text classification tasks [68]--thereby curbing the spread of propaganda by ensuring that key terms and phrases are consistently recognized and managed. _Transformer models and performance_: The superior performance of the RoBERTa model, particularly when implemented with CRF and LSTM networks and utilizing dynamic masking strategies, signifies the model's robustness [69] in processing and filtering high volumes of information within NLP tasks [67]. From the perspective of the diffusion of innovation model, these advanced models are critical for managing the rapid spread of both accurate and false information, enhancing the quality and trustworthiness of the information that is widely adopted. _Model comparison_: Comparing different models (RoBERTa, BERT, MT-DNN, XLNET, and ALBERT) with integrated NLP features [55; 56; 67; 69] highlighted the clear advantage of our ML-MC approach. Within the diffusion of innovations framework, showcasing these models' efficacy in ranking and disseminating information supports the notion that better tools significantly enhance the diffusion process by swiftly distributing valuable, truthful information to a broader audience. _Robustness checks_: Incorporating various categories and adjusting hyper-parameters to test the model's resilience [25; 65; 66] demonstrated its superior performance in propaganda prediction. The diffusion of innovations theory suggests that adaptable and robust tools are better suited for managing information in dynamic environments. This robustness ensures that our models can handle diverse scenarios effectively, improving the speed and accuracy of important information diffusion, aligning with crisis communication theory [70], and suggesting our model's application in multiple crisis scenarios. _Sentence length configuration_: Optimizing sentence lengths for Facebook and Twitter datasets led to improved accuracy in performing NLP tasks [71], particularly when shorter sentence lengths were used, with 260 characters for Facebook achieving 89% and 60 characters for Twitter achieving 76% accuracy. This emphasizes the significance of sentence length and structure in the effectiveness of word representations and NLP models [72].