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This paper introduces a computational model of Message Sensation Value (MSV) for short videos, using multimodal feature analysis to predict viewer engagement. The model, trained on 1,200 videos and validated on two unseen datasets (N=14,492), reveals that MSV positively correlates with sensory engagement but exhibits an inverted U-shaped relationship with behavioral engagement. These findings offer a quantitative framework for understanding and predicting user engagement with short-form video content.
Short videos that are *too* sensational actually decrease user engagement, revealing an inverted U-shaped relationship between "Message Sensation Value" and behavioral engagement.
The contemporary media landscape is characterized by sensational short videos. While prior research examines the effects of individual multimodal features, the collective impact of multimodal features on viewer engagement with short videos remains unknown. Grounded in the theoretical framework of Message Sensation Value (MSV), this study develops and tests a computational model of MSV with multimodal feature analysis and human evaluation of 1,200 short videos. This model that predicts sensory and behavioral engagement was further validated across two unseen datasets from three short video platforms (combined N = 14,492). While MSV is positively associated with sensory engagement, it shows an inverted U-shaped relationship with behavioral engagement: Higher MSV elicits stronger sensory stimulation, but moderate MSV optimizes behavioral engagement. This research advances the theoretical understanding of short video engagement and introduces a robust computational tool for short video research.
