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

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