Hyperstimulation in Children's Digital Content: Quantitative Computational Video Analysis with Python

Abstract

Audiovisual techniques such as highly saturated colors, rapid editing transitions, and elevated audio energy are widely employed to maximize viewer engagement in children’s media content. These productions, which fall outside conventional mainstream narrative techniques, are referred to as hyperstimuli and may encourage children to remain in front of screens for extended periods, potentially leading to various health-related problems. This study examined 90 videos with the highest view counts from 30 popular children’s YouTube channels and aimed to analyze their structural characteristics using a quantitative, comparative research design. For this purpose, a custom software tool was developed using the Python programming language, incorporating the OpenCV, NumPy, Librosa, FFmpeg, csv, and os libraries. The software sampled one frame per second and calculated the mean saturation (SAT), mean brightness/value (VAL), colorfulness (COL), visual complexity (ENT), cuts per minute (CPM), audio energy (AE), and audio spectral centroid (AC). These variables were transformed into z-scores, standardized, and combined by computing an equally weighted average to construct a comprehensive hyperstimulation index (HI) using IBM SPSS Statistics 26.0. This approach enabled the analysis of hyperstimulation levels in the sampled content based on quantitative parameters. The findings indicate that rapid editing transitions (CPM) and high audio energy (AE) constitute the strongest components of the hyperstimulation index, and view counts increase markedly as hyperstimulation levels rise. Overall, the results indicate that popular children’s YouTube channels favor narrative structures characterized by high stimulus intensity in alignment with the logic of the attention economy and that higher viewing levels are associated with this preference.

Keywords

• Digital Culture
• New Media Technologies
• Hyperstimulation
• Attention Economy
• Computer Vision

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