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A Diachronic Comparable Corpus of Spanish Digital News (2017–2026) for the Study of Stylistic Convergence in the GenAI Era

This study introduces a comparable corpus of Spanish digital news (2017–2026) designed to analyze potential linguistic shifts coinciding with the widespread adoption of Generative AI. We propose an analytical framework structured across three levels: lexical statistics, semantic topology, and neural classification. By implementing a protocol of NER-masking, we isolate structural discourse markers from topical content to identify the stylistic patterns of the contemporary period. Our results suggest a measurable structural shift within the analyzed corpus, indicating a trend toward a more standardized professional register. While macro-statistical metrics like Shannon entropy remain stable —indicating statistical consistency— Zipf-Mandelbrot distributions and SVD mapping reveal a concentration of unique vocabulary into more predictable clusters. In this scenario, the 2023–2026 subcorpus exhibits a discernible topological displacement compared to the 2017–2021 baseline. The study identifies a ‘Gray Zone’ where highly structured technical reporting and hybridized production become indistinguishable, suggesting a structural stylistic convergence within this digital environment. These findings provide a methodological baseline for analyzing discursive stabilization in professional domains without assuming definitive authorship.