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

arXiv: https://arxiv.org/abs/2604.19995 · HTML: https://mlsanity.com/paper/2604.19995/

## Key facts

- Cited by 1 papers 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.

## Builds on (most cited first, 6 of 6)

- [Temporal Segment Networks: Towards Good Practices for Deep Action Recognition](https://mlsanity.com/paper/1608.00859/) (2016)
- [HowToCaption: Prompting LLMs to Transform Video Annotations at Scale](https://mlsanity.com/paper/2310.04900/) (2023)
- [Analyzing User Engagement with TikTok's Short Format Video Recommendations using Data Donations](https://mlsanity.com/paper/2301.04945/) (2023)
- [Quantifying attention via dwell time and engagement in a social media browsing environment](https://mlsanity.com/paper/2209.10464/) (2022)
- [Catching Dark Signals in Algorithms: Unveiling Audiovisual and Thematic Markers of Unsafe Content Recommended for Children and Teenagers](https://mlsanity.com/paper/2507.12571/) (2025)
- [The Ranking Effect: How Algorithmic Rank Influences Attention on Social Media](https://mlsanity.com/paper/2509.18440/) (2025)

## Built on by (most cited first, 1 of 1)

- [Multimodal Large Language Models as Synthetic Participants in Video-Based Studies: An Evaluation](https://mlsanity.com/paper/2606.07541/) (2026)

## 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.