# Humor in Collective Discourse: Unsupervised Funniness Detection in the New Yorker Cartoon Caption Contest

arXiv: https://arxiv.org/abs/1506.08126 · HTML: https://mlsanity.com/paper/1506.08126/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Jun 2015.

## Built on by (most cited first, 16 of 16)

- [Do Androids Laugh at Electric Sheep? Humor "Understanding" Benchmarks from The New Yorker Caption Contest](https://mlsanity.com/paper/2209.06293/) (2022)
- [Humor in AI: Massive Scale Crowd-Sourced Preferences and Benchmarks for Cartoon Captioning](https://mlsanity.com/paper/2406.10522/) (2024)
- [Can Large Multimodal Models Uncover Deep Semantics Behind Images?](https://mlsanity.com/paper/2402.11281/) (2024)
- [Bridging the Creativity Understanding Gap: Small-Scale Human Alignment Enables Expert-Level Humor Ranking in LLMs](https://mlsanity.com/paper/2502.20356/) (2025)
- [Cracking the Code of Juxtaposition: Can AI Models Understand the Humorous Contradictions](https://mlsanity.com/paper/2405.19088/) (2024)
- [Creativity in AI: Progresses and Challenges](https://mlsanity.com/paper/2410.17218/) (2024)
- [Are Large Language Models Chronically Online Surfers? A Dataset for Chinese Internet Meme Explanation](https://mlsanity.com/paper/2510.00567/) (2025)
- [DeHumor: Visual Analytics for Decomposing Humor](https://mlsanity.com/paper/2107.08356/) (2021)
- [DoraCycle: Domain-Oriented Adaptation of Unified Generative Model in Multimodal Cycles](https://mlsanity.com/paper/2503.03651/) (2025)
- [When 'YES' Meets 'BUT': Can Large Models Comprehend Contradictory Humor Through Comparative Reasoning?](https://mlsanity.com/paper/2503.23137/) (2025)
- [Chumor 2.0: Towards Benchmarking Chinese Humor Understanding](https://mlsanity.com/paper/2412.17729/) (2024)
- [Beyond Single Frames: Can LMMs Comprehend Temporal and Contextual Narratives in Image Sequences?](https://mlsanity.com/paper/2502.13925/) (2025)
- [Chumor 1.0: A Truly Funny and Challenging Chinese Humor Understanding Dataset from Ruo Zhi Ba](https://mlsanity.com/paper/2406.12754/) (2024)
- [Hummus: A Dataset of Humorous Multimodal Metaphor Use](https://mlsanity.com/paper/2504.02983/) (2025)
- [Predicting Winning Captions for Weekly New Yorker Comics](https://mlsanity.com/paper/2407.18949/) (2024)
- [NYK-MS: A Well-annotated Multi-modal Metaphor and Sarcasm Understanding Benchmark on Cartoon-Caption Dataset](https://mlsanity.com/paper/2409.01037/) (2024)

## Abstract (arXiv)

The New Yorker publishes a weekly captionless cartoon. More than 5,000 readers submit captions for it. The editors select three of them and ask the readers to pick the funniest one. We describe an experiment that compares a dozen automatic methods for selecting the funniest caption. We show that negative sentiment, human-centeredness, and lexical centrality most strongly match the funniest captions, followed by positive sentiment. These results are useful for understanding humor and also in the design of more engaging conversational agents in text and multimodal (vision+text) systems. As part of this work, a large set of cartoons and captions is being made available to the community.