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arXiv · 1506.08126

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·Alejandro Jaimes·Rahul Jha·Bob Mankoff
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Abstract

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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Dragomir Radev, Amanda Stent, Joel Tetreault, Aasish Pappu, Aikaterini Iliakopoulou, Agustin Chanfreau, Paloma de Juan, Jordi Vallmitjana, Alejandro Jaimes, Rahul Jha, Bob Mankoff. 2015-06-26. Humor in Collective Discourse: Unsupervised Funniness Detection in the New Yorker Cartoon Caption Contest. https://arxiv.org/abs/1506.08126

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