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

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arxiv 1506.08126 v1 pith:PGPBRL5S submitted 2015-06-26 cs.CL cs.AIcs.MMstat.ML

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

Dragomir Radev , Amanda Stent , Joel Tetreault , Aasish Pappu , Aikaterini Iliakopoulou , Agustin Chanfreau , Paloma de Juan , Jordi Vallmitjana
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Alejandro Jaimes Rahul Jha Bob Mankoff
This is my paper
classification cs.CL cs.AIcs.MMstat.ML
keywords captionsfunniestcaptioncartoonhumorreaderssentimenttext
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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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.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. Cracking the Code of Juxtaposition: Can AI Models Understand the Humorous Contradictions

    cs.CL 2024-05 unverdicted novelty 7.0

    Introduces YesBut benchmark showing state-of-the-art multimodal models lag humans on interpreting humorous contradictions in comics.

  2. When 'YES' Meets 'BUT': Can Large Models Comprehend Contradictory Humor Through Comparative Reasoning?

    cs.CV 2025-03 unverdicted novelty 6.0

    Presents YesBut (V2) benchmark and shows state-of-the-art VLMs significantly underperform humans on tasks requiring comparative reasoning for contradictory humor in comics.