ALGORITHMIC EMOTION ENGINEERING: HOW AI CURATES EMOTIONAL STATES THROUGH SHORT-FORM MEDIA

AI & Machine Learning

Short-form media platforms powered by artificial intelligence have changed how people encounter and emotionally react to digital content. Recommendation systems no longer only match users with relevant material; they also shape feeling and attention. This paper examines the idea of algorithmic emotion engineering, understood here as the process through which AI-based recommendation systems organize emotional experiences by repeatedly promoting affective content. Drawing from media studies, psychology, and AI research, the study explains how short-form media environments influence emotional states and shape perception through personalization. Watch time, rewatch behavior, likes, comments, and shares help algorithms learn what emotional content keeps users engaged. Over time, these signals contribute to feeds that are emotionally optimized for retention and interaction. The paper shows how emotional intensity becomes central to recommendation logic. It also considers ethical concerns related to manipulation, user autonomy, and digital well-being. The paper closes by suggesting responsible design and policy responses.

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