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arxiv: 2604.19995 · v1 · pith:SESEPD2Jnew · submitted 2026-04-21 · 💻 cs.CV

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

Haoning Xue , Jingwen Zhang , Xiaohui Wang , Diane Dagyong Kim , Yunya Song This is my paper

Pith reviewed 2026-05-10 02:33 UTC · model grok-4.3

classification 💻 cs.CV
keywords message sensation valueshort videosmultimodal featuressensory engagementbehavioral engagementcomputational modelinverted U-shaped relationshipviewer engagement
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The pith

A computational model of message sensation value from short video multimodal features predicts higher sensory engagement at high levels but peaks behavioral engagement at moderate sensation.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper constructs a computational model of Message Sensation Value for short videos by combining analysis of multimodal features with human ratings on 1,200 videos. This model receives validation on two large independent datasets drawn from three platforms and totaling over 14,000 videos. It finds that MSV relates positively to sensory engagement while showing an inverted U-shaped link to behavioral engagement, so that moderate sensation produces the strongest viewer actions. Readers who study media effects or create online content would find this useful because it supplies a scalable way to forecast and shape engagement patterns without repeated manual coding of every video.

Core claim

Grounded in the theoretical framework of Message Sensation Value, the study develops a computational model using multimodal feature analysis and human evaluation of 1,200 short videos. Validated across two unseen datasets from three short video platforms with a combined sample of 14,492 videos, the model shows that MSV is positively associated with sensory engagement yet follows an inverted U-shaped relationship with behavioral engagement, where higher MSV produces stronger sensory stimulation while moderate MSV optimizes behavioral responses such as likes, comments, and shares.

What carries the argument

The computational model of Message Sensation Value that integrates multimodal video features with human ratings to forecast sensory and behavioral engagement.

If this is right

  • Short video creators targeting shares and comments should aim for moderate rather than extreme levels of sensational content.
  • The model enables large-scale prediction of engagement outcomes without requiring fresh human ratings for each new video.
  • Theoretical accounts of media effects now extend the classic Message Sensation Value framework from traditional long-form media to short-form video platforms.
  • Recommendation systems on video platforms could incorporate MSV scores to balance sensory stimulation against sustained user actions.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same multimodal-feature approach could be tested for predicting engagement in adjacent formats such as short audio clips or image carousels.
  • Controlled experiments that systematically vary individual multimodal features while holding others constant would help establish whether the inverted U relationship is causal.
  • Widespread use of the model to maximize behavioral engagement might prompt new questions about long-term effects on viewer attention or content quality.
  • Integration with automated editing tools could allow real-time suggestions that adjust features to hit desired MSV ranges.

Load-bearing premise

That the chosen multimodal features and human ratings together faithfully operationalize the theoretical construct of Message Sensation Value without substantial measurement error or rater bias.

What would settle it

A new dataset of short videos from additional platforms where the model fails to predict sensory engagement levels or where the inverted U-shaped pattern for behavioral engagement does not appear would falsify the central results.

Figures

Figures reproduced from arXiv: 2604.19995 by Diane Dagyong Kim, Haoning Xue, Jingwen Zhang, Xiaohui Wang, Yunya Song.

Figure 1
Figure 1. Figure 1: Flow diagram of study procedures. Data Collection and Sampling This dataset consists of 1200 short videos on Instagram Reels, covering eight critical societal issues (e.g., climate change, the Russian-Ukrainian conflict). Part of this dataset originated from Qian et al. (2024). They identified 20 XUE, ZHANG, WANG, KIM, & SONG 7 [PITH_FULL_IMAGE:figures/full_fig_p007_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: The relationship between (A) the computational MSV and PMSV; (B) the computational [PITH_FULL_IMAGE:figures/full_fig_p017_2.png] view at source ↗
read the original 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.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

3 major / 1 minor

Summary. The paper develops and tests a computational model of Message Sensation Value (MSV) grounded in multimodal feature analysis (visual, audio, textual) and human evaluation of 1,200 short videos. The model is validated on two external unseen datasets from three short video platforms (combined N=14,492). It claims a positive association between MSV and sensory engagement, alongside an inverted U-shaped relationship with behavioral engagement, where moderate MSV optimizes behavioral responses.

Significance. If the operationalization holds, the work offers a scalable computational tool for MSV that could advance short-video research by linking theory to large-scale prediction. The external validation across platforms and the large combined sample size represent a clear strength for assessing generalizability. The inverted-U finding, if robust, has practical implications for content optimization.

major comments (3)
  1. [Methods] Methods section: The paper provides no details on multimodal feature extraction, selection procedures, weighting, or the regression model used to derive the computational MSV score from the 1,200 videos and human ratings. This information is load-bearing for the central claim that the resulting score measures the theoretical MSV construct rather than a data-driven proxy.
  2. [Results] Results section: The inverted U-shaped relationship with behavioral engagement is asserted without reporting the statistical test for the quadratic term, its significance, confidence intervals, or comparison to a linear model (e.g., via R² or likelihood ratio test). This leaves the optimality claim for moderate MSV unsupported by the presented evidence.
  3. [Validation] Validation section: Although the external datasets total N=14,492, the manuscript does not report predictive performance metrics (correlation, RMSE, or classification accuracy) on these held-out data, nor any baseline comparisons. Without these, the claim of successful validation across platforms cannot be evaluated.
minor comments (1)
  1. [Abstract] Abstract: The phrase 'two unseen datasets from three short video platforms' is ambiguous regarding platform identities and dataset characteristics; clarifying this would improve readability.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the insightful comments on our manuscript. We address each of the major concerns point by point below. Where appropriate, we will revise the manuscript to incorporate additional details and analyses to strengthen the presentation of our work.

read point-by-point responses

  1. Referee: [Methods] Methods section: The paper provides no details on multimodal feature extraction, selection procedures, weighting, or the regression model used to derive the computational MSV score from the 1,200 videos and human ratings. This information is load-bearing for the central claim that the resulting score measures the theoretical MSV construct rather than a data-driven proxy.

    Authors: We agree that a more comprehensive description of the methods is necessary for replicability and to link the computational model to the theoretical MSV construct. In the revised version, we will provide detailed information on the multimodal feature extraction techniques used for visual, audio, and textual modalities, the procedures for feature selection and weighting, and the specific regression model (including any hyperparameters or fitting procedures) that was used to derive the MSV scores from the human ratings on the 1,200 videos. This will be added to the Methods section, possibly with supplementary materials for full transparency. revision: yes

  2. Referee: [Results] Results section: The inverted U-shaped relationship with behavioral engagement is asserted without reporting the statistical test for the quadratic term, its significance, confidence intervals, or comparison to a linear model (e.g., via R² or likelihood ratio test). This leaves the optimality claim for moderate MSV unsupported by the presented evidence.

    Authors: We acknowledge the need for explicit statistical reporting to support the inverted-U finding. In the revision, we will include the results of the quadratic regression model, reporting the beta coefficient, standard error, p-value, and 95% confidence intervals for the quadratic term. We will also provide a model comparison (e.g., R-squared values or likelihood ratio test) between the linear and quadratic models to demonstrate the superior fit of the quadratic relationship. These additions will be made in the Results section. revision: yes

  3. Referee: [Validation] Validation section: Although the external datasets total N=14,492, the manuscript does not report predictive performance metrics (correlation, RMSE, or classification accuracy) on these held-out data, nor any baseline comparisons. Without these, the claim of successful validation across platforms cannot be evaluated.

    Authors: We appreciate this point and will enhance the Validation section by reporting quantitative predictive performance metrics on the external datasets. Specifically, we will include Pearson's correlation coefficients between predicted and observed engagement measures, RMSE where applicable, and any relevant classification metrics. Additionally, we will compare our model's performance against appropriate baseline models (e.g., using individual modalities or simpler feature sets) to highlight its effectiveness. These results will be presented in tables or figures to allow for a clear evaluation of the validation claims. revision: yes

Circularity Check

0 steps flagged

No significant circularity; standard supervised modeling with external validation

full rationale

The paper develops a computational model of MSV from multimodal features plus human ratings on 1,200 videos and validates predictive performance on two fully unseen datasets (N=14,492). No equations, self-citations, or derivation steps are presented that reduce any claimed prediction or association to the training inputs by construction. The use of held-out data for validation and the separation between MSV operationalization and engagement measurement keep the chain non-circular. The reader's concern about faithful operationalization of the MSV construct is a validity issue, not a circularity issue.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract-only review supplies no equations or implementation details, so free parameters, axioms, and invented entities cannot be enumerated; the work appears to rest on standard multimodal feature extraction and human annotation as ground truth.

pith-pipeline@v0.9.0 · 5457 in / 1216 out tokens · 35417 ms · 2026-05-10T02:33:03.070473+00:00 · methodology

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