A Bibliometric Analysis of Search Engine Optimization and Generative Engine Optimization

Abstract

Generative AI-based search platforms are redefining information discovery, challenging the dominance of traditional search engine optimization (SEO) and giving rise to the concept of Generative Engine Optimization (GEO). This research aims to provide a bibliometric mapping of the SEO–GEO intersection using the PRISMA 2020 methodology. Three thematic clusters and a fragmented co-authorship network were identified. A general model of the SEO-to-GEO transition was outlined, integrating the shift from ranking-based to citation-based visibility and the need to rethink content optimization for AI readability. In addition, these results can serve as a reference for researchers and digital marketing practitioners.