YouTube Comedy Slam Preference Annotations

YouTube Comedy Slam Preference Data contains human judgments on comedy content from the YouTube platform. The dataset is hosted by the UCI Machine Learning Repository and is tagged for multimodal and LLM applications. Specific details on volume, creators, and recency are not provided.

Use Cases

• Predict comedy preference scores using features like video metadata and annotator demographics.
• Train multimodal models to correlate video or audio features from comedy slams with human judgment labels.
• Analyze social media engagement patterns by linking preference data to user interaction features.
• Benchmark LLM performance on subjective human judgment tasks related to humor and entertainment.

Strengths

• Sourced from the authoritative UCI Machine Learning Repository, ensuring a degree of curation.
• Includes human judgment labels, a key feature for training supervised models.

Limitations

• Unknown sample size prevents assessment of statistical power.
• Lack of column details limits understanding of feature richness and potential applications.
• Temporal and geographic coverage are unspecified, which may introduce bias.

Provenance

Source

UCI Machine Learning Repository

Collection Method

Likely collected via human annotation of YouTube comedy slam content, but specific methodology is unknown.