TikTok Content Strategy for Public Service Broadcasting: Balancing Algorithmic Visibility and Institutional Trust at TVRI Nasional

Abstract

The rapid platformization of digital media has compelled public service broadcasters to renegotiate audience engagement within algorithm-driven environments. TikTok, as a short-form video platform characterized by algorithmic curation and participatory culture, presents both opportunities and challenges for public institutions seeking to remain relevant among younger audiences. This study examines how TVRI Nasional, Indonesia’s public service broadcaster, developed and implemented a TikTok content strategy to enhance audience engagement while maintaining its public mandate. Using a qualitative-descriptive case study approach, the research draws on six months of participant observation (February–July 2024) within TVRI’s New Media Content Division, complemented by engagement metrics analysis across four content categories: entertainment, educational, promotional, and conversational. Content development was guided by STP (Segmentation, Targeting, Positioning) and AISAS (Attention, Interest, Search, Action, Share) frameworks, while engagement was interpretively assessed through views, likes, comments, shares, and favourites. The findings indicate that engagement on TikTok operates through multiple, coexisting logics rather than a single virality-driven hierarchy. Promotional and entertainment content were associated with higher algorithmic visibility, whereas educational and conversational content supported trust-oriented and culturally grounded engagement. Based on these empirical insights, this study proposes the Public Service Algorithmic Engagement Model (PS-AEM), which conceptualizes engagement as an interaction among algorithmic visibility, institutional trust formation, and civic–cultural resonance. This research contributes theoretically by extending digital engagement models beyond commercial logic and practically by offering a strategic framework for public service broadcasters navigating platformized media environments. Future research may apply PS-AEM comparatively across public institutions and platforms.