Metrics of Meaning: Rethinking Audience Engagement in the Age of Algorithmic Authority

Abstract

This paper investigates how algorithmic systems on platforms such as Instagram and TikTok reshape the dynamics of credibility, visibility, and meaning in digital communication. As engagement metrics: likes, shares, comments, and retention time, become central to content distribution, they no longer merely measure audience behavior but actively structure it. Drawing on theories of algorithmic mediation and audience studies, the research introduces the concept of “metrics of meaning,” a multidimensional model that integrates behavioral, cognitive, and affective indicators of engagement. Using a mixed-methods approach combining content analysis, platform observation, and interviews with journalists and content creators, the study examines how algorithmic visibility rewards emotional intensity and frequency over informational depth. Findings reveal that credibility on social media increasingly depends on affective resonance and algorithmic compliance rather than institutional legitimacy. The paper argues for a shift from quantifying audience attention to understanding its qualitative dimensions, why users engage and what meanings are produced through such interactions, highlighting implications for media literacy, digital accountability, and communication education.