When Jokes Cross the Line: Analyzing Regular Humor and Dark Humor in YouTube Shorts
- URL: http://arxiv.org/abs/2606.00046v1
- Date: Thu, 30 Apr 2026 21:45:30 GMT
- Title: When Jokes Cross the Line: Analyzing Regular Humor and Dark Humor in YouTube Shorts
- Abstract summary: TwistedHumor is a dataset of 1,211 YouTube Shorts paired with 33,041 related comments.<n>Using LLooM based concept induction over video descriptions, we find that dark humor frequently clusters around themes of critique, coping, awkwardness, and identity expression.<n>We show that regular humor is associated with more positive sentiment, while dark humor receives more mixed, neutral, and sometimes more toxic reactions.
- Score: 2.6307359124048566
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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.
Related papers
- Humor Style Drives Laughter, Topic Shapes Acceptability: Evaluating Bilingual Personal and Political Robot-Delivered AI Jokes [0.7734726150561088]
We examined the effects of humor type and joke content on perceived funniness and appropriateness, as well as preferred language.<n>Results show that humor type significantly influences funniness, with Aggressive and Affiliative humor rated higher.<n> joke content primarily affects appropriateness, with person-related jokes preferred over political ones.
arXiv Detail & Related papers (2026-06-11T12:13:43Z) - Engagement Undermines Safety: How Stereotypes and Toxicity Shape Humor in Language Models [55.98686105081078]
Large language models are increasingly used for creative writing and engagement content, raising safety concerns about the outputs.<n>This work evaluates how funniness optimization in modern LLM pipelines couples with harmful content by measuring humor, stereotypicality, and toxicity.
arXiv Detail & Related papers (2025-10-21T09:28:09Z) - From Punchlines to Predictions: A Metric to Assess LLM Performance in Identifying Humor in Stand-Up Comedy [6.124881326867511]
In light of the widespread adoption of Large Language Models, the intersection of humor and AI has become no laughing matter.<n>In this study, we assess the ability of models in accurately identifying humorous quotes from a stand-up comedy transcript.<n>We propose a novel humor detection metric designed to evaluate LLMs amongst various prompts on their capability to extract humorous punchlines.
arXiv Detail & Related papers (2025-04-12T02:19:53Z) - Can Language Models Laugh at YouTube Short-form Videos? [40.47384055149102]
We curate a user-generated dataset of 10K multimodal funny videos from YouTube, called ExFunTube.
Using a video filtering pipeline with GPT-3.5, we verify both verbal and visual elements contributing to humor.
After filtering, we annotate each video with timestamps and text explanations for funny moments.
arXiv Detail & Related papers (2023-10-22T03:01:38Z) - The Naughtyformer: A Transformer Understands Offensive Humor [63.05016513788047]
We introduce a novel jokes dataset filtered from Reddit and solve the subtype classification task using a finetuned Transformer dubbed the Naughtyformer.
We show that our model is significantly better at detecting offensiveness in jokes compared to state-of-the-art methods.
arXiv Detail & Related papers (2022-11-25T20:37:58Z) - ExPUNations: Augmenting Puns with Keywords and Explanations [88.58174386894913]
We augment an existing dataset of puns with detailed crowdsourced annotations of keywords.
This is the first humor dataset with such extensive and fine-grained annotations specifically for puns.
We propose two tasks: explanation generation to aid with pun classification and keyword-conditioned pun generation.
arXiv Detail & Related papers (2022-10-24T18:12:02Z) - Towards Multimodal Prediction of Spontaneous Humour: A Novel Dataset and First Results [84.37263300062597]
Humor is a substantial element of human social behavior, affect, and cognition.
Current methods of humor detection have been exclusively based on staged data, making them inadequate for "real-world" applications.
We contribute to addressing this deficiency by introducing the novel Passau-Spontaneous Football Coach Humor dataset, comprising about 11 hours of recordings.
arXiv Detail & Related papers (2022-09-28T17:36:47Z) - Do Androids Laugh at Electric Sheep? Humor "Understanding" Benchmarks
from The New Yorker Caption Contest [70.40189243067857]
Large neural networks can now generate jokes, but do they really "understand" humor?
We challenge AI models with three tasks derived from the New Yorker Cartoon Caption Contest.
We find that both types of models struggle at all three tasks.
arXiv Detail & Related papers (2022-09-13T20:54:00Z) - DeHumor: Visual Analytics for Decomposing Humor [36.300283476950796]
We develop DeHumor, a visual system for analyzing humorous behaviors in public speaking.
To intuitively reveal the building blocks of each concrete example, DeHumor decomposes each humorous video into multimodal features.
We show that DeHumor is able to highlight various building blocks of humor examples.
arXiv Detail & Related papers (2021-07-18T04:01:07Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.