研究机器人讲双语笑话时，风格比内容更影响笑点，但内容决定是否合适。
Humor Style Drives Laughter, Topic Shapes Acceptability: Evaluating Bilingual Personal and Political Robot-Delivered AI Jokes
- 用四种幽默风格和两类话题测试机器人讲笑话效果
- 攻击性和友好型幽默更搞笑，个人类笑话更得体
- 听众语言能力与习惯影响偏爱哪种语言版本
幽默在人际互动中至关重要，而大模型生成幽默为人机交互带来新可能。本研究采用混合因子设计，在大学课堂中让参与者评估机器人分发的AI生成笑话。考察了幽默类型（亲和型、自我增强型、攻击型、自我贬损型）和笑话内容（个人相关或政治相关）对可笑性与得体性的双重影响，以及语言偏好。结果表明：幽默类型显著影响可笑性，攻击型与亲和型得分更高；笑话内容主要影响得体性，个人类笑话更受青睐。语言偏好受内容及用户自评的语言熟练度与幽默习惯共同影响。
原文摘要 · Abstract (English)
Humor plays a central role in human social relationships, and recent advances in computational humor create new opportunities for integrating humor into human-robot interaction (HRI). While large language models (LLMs) can generate diverse forms of humor, it remains unclear how humor style, joke content, and language preference shape perceptions of robot-delivered humor in group settings. In this exploratory study, we employed a mixed factorial design in which participants evaluated AI-generated jokes delivered by a robot in a university classroom. We examined the effects of humor type (Affiliative, Self-Enhancing, Aggressive, Self-Defeating) and joke content (person-related vs. political) on perceived funniness and appropriateness, as well as preferred language. Results show that humor type significantly influences funniness, with Aggressive and Affiliative humor rated higher, while joke content primarily affects appropriateness, with person-related jokes preferred over political ones. Language preference was shaped by both joke content and participants' self-reported fluency and humor practices.
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