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...

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Détails bibliographiques

Auteurs principaux:	Mert Can Cakmak, Nitin Agarwal
Format:	Artigo
Langue:	Inglês
Publié:	SpringerOpen 2025-06-01
Collection:	Applied Network Science
Sujets:	Symbolic content Algorithmic bias Recommendation bias YouTube thumbnails YouTube recommendations Social network analysis
Accès en ligne:	https://doi.org/10.1007/s41109-025-00713-y
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