A Computational Model of Message Sensation Value in Short Video Multimodal Features that Predicts Sensory and Behavioral Engagement

arXiv:2604.19995v1 Announce Type: new Abstract: 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.

Related

• LLaVA-Octopus: Unlocking Instruction-Driven Adaptive Projector Fusion for Video Understanding
• ABMAMBA: Multimodal Large Language Model with Aligned Hierarchical Bidirectional Scan for Efficient Video Captioning
• Music Audio-Visual Question Answering Requires Specialized Multimodal Designs
• Frequency-guided Multi-level Reasoning for Scene Graph Generation in Video
• AdaSpark: Adaptive Sparsity for Efficient Long-Video Understanding

Source: arXiv cs.CV | 2026-04-23