The emergence of large language models (LLMs) and generative response systems such as AI Overviews has triggered a structural transformation of the digital information ecosystem. Traditional theoretical frameworks rooted in search engine optimization (SEO) paradigms are no longer sufficient to explain the heterogeneous distribution of visibility observed across generative platforms. This paper introduces Semantic Relativity Theory v2 (SRT), a conceptual framework proposing that meaning operates as a gravitational field in which contextual coherence and cultural resonance generate semantic mass that bends informational space. The central concept of algorithmic citability is defined as a measure of interpretive persistence in environments where algorithms function simultaneously as search engines and content generators. Empirical validation is provided through a secondary analysis of the LAIKA study (Cachón, 2025), which examined twelve Spanish media outlets affected by AI Overviews, revealing traffic variations ranging from −44.6% to +45.8%—fluctuations unaccounted for by conventional SEO metrics. The findings show that semantic gravity predicts resistance to algorithmic volatility: content with high contextual density and conceptually citable structures maintains resonance even as direct visibility declines. The theory incorporates the CHORDS/CS operational model (Context, Harmony, Orchestration, Rhythm, Distinctive Voice, Society/Code-Switching) as a descriptive framework for semantic field analysis. This work establishes an epistemological foundation for understanding the redistribution of meaning within hybrid human–machine systems.
Lopez Lopez José (Fri,) studied this question.