Algorithmic Recommendation Systems and Adolescent Media Literacy in Global Cities: A Comparative Study of Toronto, Berlin, and Bangkok

Authors

  • Pimchanok Srisawat

    Faculty of Communication Arts, Chulalongkorn University, Bangkok 10330, Thailand

Keywords:

Algorithmic recommendation systems; Adolescent media literacy; Global cities; Cultural context; Platform regulation; Cognitive biases; TikTok; YouTube; Misinformation; Critical evaluation

Abstract

This study explores how social media (TikTok, YouTube, Instagram) algorithmic recommendation systems (ARS) shape 13–17-year-olds’ media literacy (access, analyze, evaluate, create content) in Toronto (Canada), Berlin (Germany), Bangkok (Thailand). Using mixed methods—surveys of 4,500 adolescents, 180-participant focus groups, content analysis of 2,000 ARS videos—it identifies cross-cultural differences in ARS exposure, cognitive biases, and media literacy. Results: Berlin’s strict ARS regulation (e.g., EU Digital Services Act) and mandatory media literacy education bring the highest critical skills (78% spot algorithmic bias); Toronto (moderate regulation/optional education) has 65%; Bangkok (limited regulation/fragmented education) 42%. TikTok’s short videos lower analysis depth; YouTube’s longer ones boost fact-checking. It develops an “Algorithmic Media Literacy Framework” for educators, policymakers, developers.

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Issue

Vol. 1 No. 1 (September 2025)

Section

Articles