Pith Number

pith:SESEPD2J

pith:2026:SESEPD2JYLX33HBB7K4TBMOIKU

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

Diane Dagyong Kim, Haoning Xue, Jingwen Zhang, Xiaohui Wang, Yunya Song

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.

arxiv:2604.19995 v1 · 2026-04-21 · cs.CV

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Record completeness

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3 Author claim open · sign in to claim

4 Citations open

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The bundle contains the canonical record plus signed events. A mirror can host it anywhere and recompute the same current state with the deterministic merge algorithm.

Claims

C1strongest claim

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.

C2weakest assumption

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.

C3one line summary

A computational MSV model built from short-video multimodal features positively predicts sensory engagement but shows an inverted-U relationship with behavioral engagement, validated on large unseen platform datasets.

References

18 extracted · 18 resolved · 0 Pith anchors

[1] ComplexityMeasures.jl: Scalable soft- ware to unify and accelerate entropy and complexity timeseries analysis 2024 · doi:10.1371/journal.pone

[2] In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp 2000 · doi:10.1145/2939672.2939785

[3] Epstein, Z., Lin, H., Pennycook, G., & Rand, D. (2022). Quantifying attention via dwelltimeandengagementinasocialmediabrowsingenvironment[_eprint: 2209.10464] 2022

[4] W., & Palmgreen, P 1995 · doi:10.1207/s15327027hc0703_3

[5] Fushiki, T. (2011). Estimation of prediction error by using K-fold cross-validation. StatisticsandComputing,21(2),137–146.https://doi.org/10.1007/s11222-009- 9153-8 GoogleCloud. (2024). Video AI. Retr 2011 · doi:10.1007/s11222-009-

Receipt and verification

First computed	2026-05-25T02:01:20.882934Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

9124478f49c2efbd9c21fab930b1c8552460b5b6607b24e12d2d1e4bcefc5fc4

Aliases

arxiv: 2604.19995 · arxiv_version: 2604.19995v1 · doi: 10.48550/arxiv.2604.19995 · pith_short_12: SESEPD2JYLX3 · pith_short_16: SESEPD2JYLX33HBB · pith_short_8: SESEPD2J

Agent API

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/SESEPD2JYLX33HBB7K4TBMOIKU \
  | jq -c '.canonical_record' \
  | python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: 9124478f49c2efbd9c21fab930b1c8552460b5b6607b24e12d2d1e4bcefc5fc4

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "d2bb208a66cf048e29563e46a5261cb43010aa68f2996d4f04c79986023839e6",
    "cross_cats_sorted": [],
    "license": "http://creativecommons.org/licenses/by/4.0/",
    "primary_cat": "cs.CV",
    "submitted_at": "2026-04-21T21:09:43Z",
    "title_canon_sha256": "111651068d738ad61105cd230896528c1d2b5a7ce6e3685781b351b8fa476a7e"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2604.19995",
    "kind": "arxiv",
    "version": 1
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}