인공지능 기반의 영상 분석 프레임워크를 활용한 미술관 관람 경험 관련 인스타그램 릴스 게시물 분석 연구: 홍보용 및 감상용 콘텐츠 비교 분석을 중심으로

초록

This study analyzes museum visitor experiences on Instagram Reels using a multi-stage framework integrating scene segmentation, classification, and Markov transition analysis. Comparing 1,077 scenes, findings reveal that appreciation-oriented Reels foster aesthetic engagement through continuity in ‘artwork’ and ‘interaction’ scenes. In contrast, promotional Reels prioritize information delivery via frequent transitions and ‘selfie’ and ‘space’ imagery. Statistical analysis confirms significant associations between Reel types and scene composition, demonstrating that Reels function as purposeful narrative devices that selectively mediate visitor experiences through strategic editing. This research offers a robust quantitative methodology for studying digital museum engagement and marketing.

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