首个日式漫才观众反应多模态数据集，揭示不同观众类型与观看顺序效应。
ManzaiSet: A Multimodal Dataset of Viewer Responses to Japanese Manzai Comedy
- 采集241人观看专业漫才表演的面部与音频数据，按随机顺序播放至多10场。
- 发现72.8%观众为稳定喜爱者，13.2%为低且波动型拒绝者，14.0%为逐渐改善型。
- 观众反应受观看顺序影响，越往后越积极，挑战疲劳假说，适合跨文化情感计算研究。
我们提出ManzaiSet，首个大规模多模态观众对日本漫才喜剧的反应数据集，包含241名参与者在随机顺序下观看最多10场专业表演的面部视频与音频（94.6%至少观看8场；分析聚焦于n=228）。该数据集旨在缓解情感计算中的西方中心偏见。三项关键发现：(1) k均值聚类识别出三类观众：高且稳定的欣赏者（72.8%，n=166）、低且波动的拒绝者（13.2%，n=30）以及波动改善者（14.0%，n=32），方差异质性显著（Brown-Forsythe p < 0.001）；(2) 个体层面分析显示观看顺序呈正向效应（平均斜率=0.488，t(227)=5.42，p<0.001，置换检验p<0.001），反驳疲劳假设；(3) 基于自动化幽默分类（77个实例，131个标签）与观众反应建模，经FDR校正后未发现各类型间差异。该数据集支持文化敏感的情感人工智能开发及非西方语境下的个性化娱乐系统。
原文摘要 · Abstract (English)
We present ManzaiSet, the first large scale multimodal dataset of viewer responses to Japanese manzai comedy, capturing facial videos and audio from 241 participants watching up to 10 professional performances in randomized order (94.6 percent watched >= 8; analyses focus on n=228). This addresses the Western centric bias in affective computing. Three key findings emerge: (1) k means clustering identified three distinct viewer types: High and Stable Appreciators (72.8 percent, n=166), Low and Variable Decliners (13.2 percent, n=30), and Variable Improvers (14.0 percent, n=32), with heterogeneity of variance (Brown Forsythe p < 0.001); (2) individual level analysis revealed a positive viewing order effect (mean slope = 0.488, t(227) = 5.42, p < 0.001, permutation p < 0.001), contradicting fatigue hypotheses; (3) automated humor classification (77 instances, 131 labels) plus viewer level response modeling found no type wise differences after FDR correction. The dataset enables culturally aware emotion AI development and personalized entertainment systems tailored to non Western contexts.
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