## 5 Discussion
### Theoretical Implications  
This study makes significant theoretical contributions to the field of propaganda detection and classification, informed by the findings and the diffusion of innovations theory, which offers a robust framework for understanding the adoption and diffusion of advanced methodologies.  
_First_, this research advances the theoretical understanding of propaganda detection by emphasizing the complexity of identifying subtle cues and concealed messages within textual data. Unlike previous studies that predominantly focused on document-levelanalysis [6; 12; 73], our work adopts a fine-grained ML-MC framework capable of detecting six distinct propaganda techniques and six COVID-19-related themes at the sentence level. This granularity allows for more nuanced filtering and classification, especially on social media platforms. The integration of the diffusion of innovations theory highlights the practical advantages of this approach, as it equips both IS researchers and general users to detect propaganda early, promoting the rapid dissemination of accurate and trustworthy information.  
_Second_, this study introduces the ProText dataset, which addresses key gaps in prior datasets such as PTC [11] and Proppy [7] that lacked comprehensive fact-checking and fine-grained labeling. ProText is meticulously curated at the sentence level, incorporating balanced annotations for both propaganda techniques and themes. This dataset not only serves as a valuable resource for IS researchers but also contributes to a deeper theoretical understanding of propaganda's motivations, which extend beyond personal or organizational gains to societal impacts. By integrating traditional ML and advanced DL techniques, the dataset offers new opportunities for theoretical exploration within AI and IS research, while aligning with the diffusion of innovations theory to foster the rapid adoption of these methodologies in diverse domains.  
_Third_, this study introduces a novel ML-MC model that bridges the theoretical gap between multi-label (ML) and multi-class (MC) tasks. This model provides a detailed framework for addressing challenges, such as media bias and fact-checking, while demonstrating clear advantages over traditional methods [74; 75]. By unifying ML and MC tasks, this approach not only enhances the understanding of propaganda classification but also facilitates quicker adoption by showcasing its relative advantage, as emphasized in the diffusion of innovations theory. This contribution is especially relevant in crisis scenarios, such as natural disasters or political upheavals, where accurate and timely information dissemination is critical.  
_Finally_, this research enhances the theoretical understanding of propaganda mechanisms, enabling IS researchers and practitioners to critically evaluate and counteract public opinion manipulation on social media platforms. By moving beyond traditional classification methods [57; 64], the study underscores the importance of promoting user awareness and vigilance in digital environments. It introduces tools that enhance digital citizenship by safeguarding user privacy and fostering social equality while upholding freedom of speech. These advancements align with the diffusion of innovations theory by highlighting the innovation's visibility and relative advantage, encouraging widespread adoption in the fight against misinformation.  
These findings are critical in restricting cybercrime and preventing fraud on digital platforms, ensuring that entities like Facebook and Twitter remain accountable for user privacy and digital integrity. They provide a robust reference point for future IS research, emphasizing the role of advanced DL techniques in safeguarding societal and digital ecosystems while fostering equitable and transparent communication channels.