## 6 Conclusions, Limitations, and Future Directions  
During the COVID-19 pandemic, social media platforms emerged as a critical medium for disseminating information. However, these platforms also facilitated the spread of fake news and propaganda, highlighting the need for effective tools to combat misinformation. This study introduces a fine-tuned DL model based on RoBERTa, specifically designed for detecting propaganda in text using ML-MC classification. By leveraging public discussions on COVID-19, the study compares various approaches, including LSTM, CRF, \(n\)-grams, and TF-IDF, ultimately demonstrating the superior performance of the proposed RoBERTa-based framework. Rigorous experimental optimization and modeling evaluations further reveal the robustness and accuracy of the approach, with LSTM showing exceptional effectiveness in handling context-sensitive propaganda detection. The findings highlight the model's clear relative advantages, compatibility with existing systems, and high efficacy, increasing its potential for widespread adoption. The integration of the diffusion of innovations theory emphasizes the importance of showcasing observable benefits and minimizing complexity to facilitate the adoption of advanced propaganda detection methodologies. In conclusion, this study provides a solid foundation for advancing propaganda detection methodologies, contributing significantly to theoretical, practical, and societal domains. These advancements provide critical support for IS researchers and practitioners in identifying and mitigating propaganda on social media platforms while providing valuable data and insights for future research. Despite its contributions, this study has certain limitations that warrant further exploration. _First_, the availability of labeled propaganda datasets remains a critical challenge. Annotating extensive and diverse datasets requires substantial manual effort and expert input, limiting scalability. Future research should prioritize the development of automated ML models to facilitate efficient and accurate dataset labeling, enabling the processing of larger training data volumes. This advancement would streamline the creation of robust datasets and enhance the generalizability of propaganda detection systems. _Second_, propaganda techniques vary significantly across domains, languages, and cultures, which may affect the applicability of findings in broader contexts. Expanding research to include multi-lingual and cross-cultural propaganda detection is essential for addressing these disparities. Future efforts should focus on building models capable of detecting propaganda in multiple languages and adapting to the specific linguistic and cultural nuances of diverse communities. This direction is critical for combating misinformation campaigns targeting global audiences. _Third_, while DL models like ML-MC demonstrate impressive performance, their complexity can hinder interpretability. Understanding the specific linguistic cues and features driving propaganda detection remains a challenge, limiting insights into the underlying mechanisms. To address this, future research should explore incremental or lifelong learning techniques, enabling models to adapt continuously to new and evolving propaganda strategies. Such advancements would ensure the system remains robust and up-to-date, effectively tackling emerging misinformation threats. Conceptualization, methodology, software, and validation, P.N.A. and A.M.S. ; formal analysis and investigation, P.N.A. ; resources, A.M.S. ; data curation, P.N.A. ; writing--original draft preparation, P.N.A. and A.M.S. ; writing--review and editing, P.N.A., A.M.S. and K.L. ; visualization, A.M.S. ; supervision, K.L. ; project administration, A.M.S. All authors have read and agreed to the published version of the manuscript. This work was supported by the Gachon University research fund of 2024. (GCU-202404180001).