mlsanityData to 14 Oct 2026

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

arXiv 2604.19995 ↗First posted 21 Apr 2026cs.CVcited by 1

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Abstract (arXiv)

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.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 6 of its 69 references that are in the map. First posted on arXiv 21 Apr 2026.

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6 of 69 references.

References · 6

  1. 2016Temporal Segment Networks: Towards Good Practices for Deep Action Recognitioncited by 902
  2. 2023HowToCaption: Prompting LLMs to Transform Video Annotations at Scalecited by 34
  3. 2023Analyzing User Engagement with TikTok's Short Format Video Recommendations using Data Donationscited by 8
  4. 2022Quantifying attention via dwell time and engagement in a social media browsing environmentcited by 4
  5. 2025Catching Dark Signals in Algorithms: Unveiling Audiovisual and Thematic Markers of Unsafe Content Recommended for Children and Teenagerscited by 1
  6. 2025The Ranking Effect: How Algorithmic Rank Influences Attention on Social Mediacited by 1

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Citing papers per year: 2026: 1.

1 paper.

  1. 2026Multimodal Large Language Models as Synthetic Participants in Video-Based Studies: An Evaluationcited by 0