SOURCE-LINKED INTELLIGENCE

MultiHuSE: A Multimodal Dataset for Humour Styles and Emotions

arXiv · AI, language, vision and robotics · article · Sep 10, 2026 · UTC

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

Read original source ↗ Open in workspace

recordType

paper

region

Global