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Abstract - A Computational Model of Message Sensation Value in Short Video Multimodal Features that Predicts Sensory 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.
短视频多模态特征中信息感知价值的计算模型:预测感官与行为参与度 /
A Computational Model of Message Sensation Value in Short Video Multimodal Features that Predicts Sensory and Behavioral Engagement
1️⃣ 一句话总结
本研究基于信息感知价值理论,通过分析1200个短视频的多模态特征并构建计算模型,发现短视频的感官刺激(如视听冲击)与用户的感官参与度呈正相关,但与行为参与度(如点赞、分享)呈倒U型关系——即适度的感官刺激最能促使用户采取实际行动。