Attention Economics in the Age of Artificial Intelligence: The Impact of Algorithmic Groove Music Recommendations on Social Media Users

Abstract

This research examines the relationship between groove-oriented short-form video content, user engagement behavior, and recommendation-driven consumption in social media environments. The study combines a within-subject video experiment with an international survey to investigate engagement-related behaviors including enjoyment, replay intention, song-search behavior, continued consumption of similar content, and perceived algorithmic influence. The research is framed as a human–algorithm feedback loop in which content presentation may influence engagement signals, which may in turn be associated with further recommendation exposure and repetitive consumption. The study does not claim access to, reconstruction of, or causal identification of proprietary platform-ranking algorithms. Findings are interpreted within the methodological limitations of the experimental and survey designs.