Video platforms such as YouTube have reshaped how users engage with entertainment and information, emphasizing brief, highly engaging content such as Shorts. Within this ecosystem, certain content occupies a gray area where it remains allowed but may still have unintended negative effects on some audiences. To study this problem, we introduce TwistedHumor, a dataset of 1,211 YouTube Shorts paired with 33,041 related comments, with hand annotations for humor presence, humor type, harm, topic, rhetorical devices, and stand up context. Beyond dataset creation, we present a multi view analysis of how humor and harm appear in short form social media. Using LLooM based concept induction over video descriptions, we find that dark humor frequently clusters around themes of critique, coping, awkwardness, and identity expression rather than appearing as a single uniform category. We further analyze audience response through linked comments and show that regular humor is associated with more positive sentiment, while dark humor receives more mixed, neutral, and sometimes more toxic reactions. Finally, we evaluate large language models against human annotations and find that they perform better on stand up comedy compared to shorter jokes. Together, these results position TwistedHumor not only as a new benchmark, but as an empirical study of the gray area between humor and harm in short form video, highlighting the need for context aware moderation and more robust multimodal evaluation.

翻译：诸如YouTube等视频平台已重塑用户与娱乐及信息的互动方式，尤其强调像Shorts这类简短且高吸引力的内容。在此生态系统中，某些内容处于灰色地带——虽被平台允许，却可能对部分观众产生意外的负面影响。为研究此问题，我们构建了TwistedHumor数据集，包含1,211个YouTube短视频及33,041条相关评论，并手工标注了幽默存在性、幽默类型、伤害性、主题、修辞手法及单口喜剧语境。除数据集创建外，我们提出多视角分析框架，探究幽默与伤害在短视频社交媒体中的呈现形式。通过基于LLooM的视频描述概念归纳，我们发现黑色幽默常围绕批评、应对、尴尬及身份表达等主题聚类，而非呈现单一统一类别。我们进一步通过关联评论分析观众反应，表明常规幽默与更积极的情感倾向相关，而黑色幽默则引发更多混合、中性甚至毒性更强的反应。最后，我们将大型语言模型评估结果与人工标注对比，发现它们在单口喜剧上的表现优于简短笑话。这些结果共同将TwistedHumor定位为不仅是新基准，更是对短视频中幽默与伤害之间灰色地带的实证研究，凸显了上下文感知审核及更鲁棒的多模态评估的必要性。