GREEN CHEMISTRY, DIGITAL PLATFORMS, AND ALGORITHMIC AMPLIFICATION: AN AI-DRIVEN EXAMINATION OF SUSTAINABILITY DISCOURSES AND LEARNING IN ONLINE MEDIA

Abstract

Green Chemistry offers a framework of design principles aimed at minimizing waste, reducing hazard, and enhancing efficiency in chemical processes. While these principles have advanced significantly in laboratory contexts, their public visibility and societal impact are increasingly mediated by digital platforms where algorithmic amplification shapes discourse. This study presents an interdisciplinary methodology combining artificial intelligence, computational chemistry, media analytics, and social science to examine how Green Chemistry is represented, clustered, and interpreted in online media. Data were collected from Twitter, YouTube, Reddit, news outlets, and blogs. Natural language processing techniques, including topic modeling, transformer embeddings, and sentiment classification, revealed distinct discursive clusters and platform-specific tonal orientations. Supervised machine learning models, particularly XGBoost, SVM, and BERT, achieved high predictive accuracy in classifying sentiment and framing, underscoring the value of AI-driven approaches for large-scale media analysis. Molecular docking experiments were conducted to visually anchor media discourses in material chemical processes, illustrating the scientific reality of renewable feedstocks and safer solvent design. Network analysis identified central hashtags and influencers driving algorithmic amplification, while statistical testing confirmed significant sentiment variation across platforms. By integrating AI, chemistry, media, and social science perspectives, this study advances understanding of how sustainability discourses are produced, amplified, and contextualized. The findings highlight both the potential and the limitations of media-driven engagement with Green Chemistry, offering insights for researchers, educators, and policymakers committed to enhancing sustainability communication.