AI-Driven Analysis of Code-Switching from Local European Languages to English in Media and Literary Discourse

Abstract

English has become a dominant global language, reshaping linguistic practices throughout Europe. In media and literature, code-switching—the alternation between local languages and English—has grown increasingly common. This study employs advanced AI-driven Natural Language Processing techniques to analyze the patterns, motivations, and sociolinguistic implications of this phenomenon.

Our research draws on diverse sources, including newspapers, social media posts, television scripts, films, and digital literature from several European regions. By using state-of-the-art machine learning models, we automatically detect instances of code-switching and examine the contexts in which these shifts occur. This approach allows us to classify language blending based on cultural, social, and genre-specific factors. Early results indicate that code-switching is often employed to convey modernity, express technical ideas, or create a particular social tone, with variations observed across different media and regions. Furthermore, our study integrates quantitative analysis with qualitative insights by reviewing selected case studies in depth. This mixed-methods strategy enriches our understanding of the relationship between language choice and cultural identity. The findings shed light on how the increasing use of English influences local linguistic landscapes and may signal broader trends in language evolution. Our research not only highlights the transformative role of AI in sociolinguistic studies but also offers practical implications for educators, media professionals, and policymakers. By clarifying the dynamics of code-switching, we hope to contribute to efforts aimed at preserving linguistic diversity while embracing the benefits of global communication. In sum, this work demonstrates that AI is a powerful tool for uncovering complex language phenomena, offering fresh perspectives on how European languages adapt in a rapidly globalizing media environment. This comprehensive AI-driven analysis not only deepens our understanding of multilingual dynamics but also informs future strategies for language education and media production, ensuring that cultural nuance is maintained amid global linguistic shifts in diverse European contexts.