A Computational Model of Message Sensation Value in Short Video Multimodal Features that Predicts Sensory and Behavioral Engagement
This research addresses the problem of understanding viewer engagement for short video platforms, offering a computational tool for analysis, but it is incremental as it builds on existing MSV theory.
The study developed a computational model based on Message Sensation Value (MSV) to predict sensory and behavioral engagement in short videos, analyzing 1,200 videos and validating on datasets with 14,492 videos, finding that higher MSV increases sensory engagement but moderate MSV optimizes 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.