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

2026-04-21

AI Summary

This study addresses the lack of systematic understanding regarding how multimodal features of short videos jointly influence users’ sensory experiences and behavioral engagement. Grounded in Message Sensory Value (MSV) theory, the work integrates multimodal feature extraction, large-scale human annotation, and computational modeling to develop and validate a cross-platform generalizable engagement prediction model. It reveals, for the first time, an inverted U-shaped relationship between MSV and behavioral engagement, indicating that moderate levels of sensory value maximize user participation. The model demonstrates robust performance across 14,492 unseen videos from three distinct platforms, accurately predicting both sensory stimulation and engagement levels. These findings provide a theoretical foundation and practical tool for optimizing short-video content design and evaluation.

Technology Category

• Computer Vision: Multi-modal Vision
• Machine Learning: Multimodal Learning
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• User Modeling, Personalization and Recommendation: Metrics for user behavior and evaluating success
• Web Mining and Content Analysis: Mining multimedia, multimodal, multilingual, cross-lingual Web data
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

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.

Problem

• Message Sensation Value
• short video
• multimodal features
• sensory engagement
• behavioral engagement

Innovation

• Message Sensation Value
• multimodal features
• computational model
• sensory engagement
• behavioral engagement