Comparative Keyword Network Analysis of Korean-Language Algorithmic Recommendation Discourses in AI Related to TikTok and YouTube

This study investigates how artificial intelligence (AI) is represented within Korean-language recommendation algorithm discourse on TikTok and YouTube. To examine the structural characteristics and discourse tendencies of AI-related discussions, the study applies text mining and keyword network analysis methods, including TF, TF-IDF analysis, centrality analysis, CONCOR clustering, and sentiment analysis. The findings indicate that AI occupies a central position within recommendation algorithm discourse and is strongly associated with algorithms, data, content recommendation, and technological systems across both platforms. The analysis further reveals notable differences between the two platforms: TikTok discourse demonstrates a stronger emphasis on automation and technological mechanisms, whereas YouTube discourse is more closely associated with content production, commercialization, and educational contexts. In addition, public discourse surrounding AI-driven recommendation systems reflects both positive-oriented and concern-related perspectives regarding technological innovation, platform influence, and social implications. This study contributes to a broader understanding of how AI is socially interpreted and represented within contemporary digital platform discourse.