Individual Recognition on Generative AI: A Spotify-Based Empirical Analysis of Digital Attention Signals

Abstract

This study develops a proxy-based empirical framework for examining individual recognition of generative artificial intelligence (GenAI) through behavioral traces from Spotify’s public chart environment. Rather than relying on self-reported intentions alone, the paper interprets platform-level listening as an observable recognition outcome and uses Spotify global daily chart data to identify how visibility, momentum, lifecycle, and semantic cues shape digital attention. A hand-curated sample of 117 tracks from the Spotify Global Daily Chart (27 March 2026) is constructed from publicly accessible chart records and transformed into a structured econometric dataset. The analysis combines descriptive statistics, mean-comparison tests, ordinary least squares estimation, and logistic regression. Results show that historical peak rank is the strongest and most stable predictor of current recognition, while legacy status weakens the probability of remaining in the top recognition quartile. Semantic cues linked to a GenAI-related proxy vocabulary do not produce a statistically significant premium after platform-performance controls are introduced, suggesting that recognition in algorithmic cultural markets is driven more by visibility history and momentum than by symbolic labels alone. The paper contributes a digital-trace perspective to the emerging literature on GenAI recognition, proposes a tractable proxy design for future research, and offers practical implications for platform strategists, digital marketers, and creators seeking to position AI-related content in attention-intensive media ecosystems.