Influence of symbolic content on recommendation bias: analyzing YouTube’s algorithm during Taiwan’s 2024 election

Abstract

This study investigates the role of symbolic content, including social, cultural, and political imagery, in shaping algorithmic biases within YouTube’s recommendation system, using the 2024 Taiwanese presidential election as a case study. Leveraging classification methodology and a dataset of 15,600 videos collected via a rigorous multiphase keyword expansion, our research employs a novel combination of social network analysis, statistical metrics, and generative AI-based content evaluation to examine the propagation dynamics, community formation, and topic relevance of both symbolic and non-symbolic content. Our analysis reveals a dual dynamic: symbolic content fosters tightly integrated, cohesive communities characterized by strong thematic consistency and deeper topic relevance, yet exhibits limited network-wide visibility, while non-symbolic content achieves broader connectivity by often serving as crucial bridges within recommendation networks. Building on prior research documenting the influential role of symbols in political mobilization and online misinformation, we further assess how symbolic imagery interacts with algorithmic recommendation processes. Our findings underscore that algorithmic biases may inadvertently reinforce echo chambers and limit content diversity, highlighting the need for recommendation systems that balance content relevance with community-specific thematic coherence. These insights offer valuable guidance for policymakers, platform designers, and content creators striving for equitable content representation in the digital era.