Topic-Sensitive Differentiation and Algorithmic Equivalence on Short-Form Video Platforms: A Systematic Review of Visual Frameworks

The meteoric rise of short-form video platforms has fundamentally transformed digital media consumption patterns, with content recommendation algorithms serving as powerful arbiters of information dissemination. These algorithms curate vast arrays of content—from entertainment to news to political discourse—raising critical questions about their uniform approach to content recommendation. Through a systematic review of 148 algorithmic models from 123 academic studies published between 2015 and 2025, we investigate how recommendation systems handle different types of content on platforms like TikTok, YouTube Shorts, and Instagram Reels. Our analysis introduces a novel theoretical framework distinguishing between algorithmic equivalence — the practice of applying identical recommendation strategies across all content types regardless of thematic differences — and topic-sensitive differentiation — where systems adjust recommendation strategies based on content themes (e.g., distinguishing political from entertainment content). We find that 142 of 148 models (95.9%) exhibit algorithmic equivalence, suggesting a systemic lack of content-type differentiation in recommendation strategies. Our findings suggest that the prevailing one-size-fits-all algorithmic approach may obscure critical public-interest content, and shape user perceptions in unintended ways. The findings highlight a critical need for more adaptive and context-aware recommendation systems.