INTERPRETIVE JOURNALISM ANALYSIS OF AUDIENCE ENGAGEMENT PATTERNS ACROSS THE WEBS (1.0, 2.0, & 3.0)

Abstract

This study interrogated the shifting dynamics of audience behaviour within the evolving architecture of Web 1.0, Web 2.0, and Web 3.0, foregrounding interpretive journalism as a critical lens for understanding meaning-making in digitally mediated environments; the aim was to examine how audiences interpret news, how platform affordances shape engagement, and how interpretive framing influences public discourse across these three webs. Anchored on Uses and Gratifications Theory, the study adopted a desk-based qualitative cross-sectional descriptive design, systematically analysing 30 purposively selected studies (10 per web category) sourced from Google Scholar, Scopus, JSTOR, and Web of Science, with data subjected to thematic coding and comparative analysis. Findings reveal a tripartite evolution: passive cognition in Web 1 (33.3%), social co-construction in Web 2 (33.3%), and algorithmically mediated interpretation in Web 3 (33.3%), alongside a transition in engagement from low interactivity to prosumerism and ultimately to predictive, immersive participation driven by AI systems. Furthermore, interpretive framing shifts from institutional gatekeeping to negotiated discourse and finally to algorithmic curation, resulting in increasing fragmentation and polarization of public discourse. The study concludes that interpretive journalism now operates within a complex socio-technical matrix where audience agency is simultaneously expanded and constrained by platform logics. It recommends: (1) that journalists enhance contextual depth and verification practices, (2) that platforms ensure algorithmic transparency and diversity exposure, and (3) that audiences cultivate critical media literacy to navigate personalised information ecosystems.