MultiHuSE: A Multimodal Dataset for Humour Styles and Emotions — Paper

Abstract

Computational recognition of verbal humour remains a challenging task, requiring an understanding of language, delivery style, emotions, and cultural context. Most existing approaches focus on binary classification and lack datasets that capture psychological dimensions of humour alongside variations in expression. We introduce MultiHuSE, a multimodal dataset comprising 2,407 high-definition videos of 50 demographically diverse actors performing 1,463 text samples across four psychological humour styles (affiliative, aggressive, self-enhancing, and self-deprecating), as well as neutral content. A subset is additionally annotated for underlying emotions. The dataset uniquely captures multiple actor interpretations of the same texts, enabling systematic analysis of expressive diversity. Baseline experiments show that multimodal fusion outperforms unimodal approaches (80.1% vs. 77.4% accuracy) in humour style classification, with particularly strong gains for affiliative humour (66% to 74%). While text provides the strongest individual signal, fusion models deliver meaningful improvements. We hope that MultiHuSE provides empirical support for psychological theories linking humour and emotion, while also opening new avenues for research in human communication, well-being, and AI-driven interaction. The dataset is available for academic use under an End-User Licence Agreement.

Authors 3

Mary Ogbuka Kenneth, Foaad Khosmood, Abbas Edalat

Specification

Official page: arxiv.org/abs/2609.11322
Source:arXiv (Atom API + RSS)T1observed 55 min agohigh
Arxiv announce type: cross
Source:arXiv (Atom API + RSS)T1observed 54 min agohigh
arXiv id: 2609.11322
Source:arXiv (Atom API + RSS)T1observed 55 min agohigh
Categories: cs.CL, cs.CV, cs.MM
Source:arXiv (Atom API + RSS)T1observed 55 min agohigh
DOI: 10.1109/CBMI66578.2025.11339313
Source:arXiv (Atom API + RSS)T1observed 55 min agohigh
PDF: arxiv.org/pdf/2609.11322
Source:arXiv (Atom API + RSS)T1observed 55 min agohigh
Primary category: cs.CL
Source:arXiv (Atom API + RSS)T1observed 55 min agohigh
Published: 11 Sept 2026
Source:arXiv (Atom API + RSS)T1observed 55 min agohigh

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Provenance

Attributed facts

10

Source tiers

T110

Freshest observation

54 min ago

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None

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Authors: Mary Ogbuka Kenneth, Foaad Khosmood, Abbas Edalat

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Claim history · Abstract

1 claims · 1 propertiesShow all properties

Abstractabstract1

Claim history for Abstract
Value	Valid from → to	Status	Source	Confidence	Extractor
Computational recognition of verbal humour remains a challenging task, requiring an understanding of language, delivery style, emotions, and cultural context. Most existing approaches focus on binary classification and lack datasets that capture psychological dimensions of humour alongside variations in expression. We introduce MultiHuSE, a multimodal dataset comprising 2,407 high-definition videos of 50 demographically diverse actors performing 1,463 text samples across four psychological humour styles (affiliative, aggressive, self-enhancing, and self-deprecating), as well as neutral content. A subset is additionally annotated for underlying emotions. The dataset uniquely captures multiple actor interpretations of the same texts, enabling systematic analysis of expressive diversity. Baseline experiments show that multimodal fusion outperforms unimodal approaches (80.1% vs. 77.4% accuracy) in humour style classification, with particularly strong gains for affiliative humour (66% to 74%). While text provides the strongest individual signal, fusion models deliver meaningful improvements. We hope that MultiHuSE provides empirical support for psychological theories linking humour and emotion, while also opening new avenues for research in human communication, well-being, and AI-driven interaction. The dataset is available for academic use under an End-User Licence Agreement.	11 Sept 2026 21:03 UTC → current	current	arXiv (Atom API + RSS)T1	high	deterministic

Claims are temporal and append-only: a new observation closes the previous claim (valid_to) instead of overwriting it. Conflicting claims from different sources are kept side by side and flagged — never averaged. Methodology →

11 Sept 2026
Property changedPaperMultiHuSE: A Multimodal Dataset for Humour Styles and Emotions
MultiHuSE: A Multimodal Dataset for Humour Styles and Emotions: arxiv announce type changed from new to cross
Arxiv announce typenew→crossarxiv
rss.arxiv.org
11 Sept 2026
New paperPaperMultiHuSE: A Multimodal Dataset for Humour Styles and Emotions
New paper: MultiHuSE: A Multimodal Dataset for Humour Styles and Emotions
arxiv
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Source documents
Source	Document	Type	Tier	Last observed	Snapshots
arXiv (Atom API + RSS)	rss.arxiv.org/rss/cs.CV	feed	T1· Official	54 min ago	1
arXiv (Atom API + RSS)	rss.arxiv.org/rss/cs.CL	feed	T1· Official	55 min ago	1

Tier 1 = official/primary, 2 = quality secondary, 3 = community, 4 = unverified. Every snapshot is archived; see all sources and the methodology.