The New Yorker uses NEXT to crowd source the next caption contest winner

Kevin Jamieson

Each week, the New Yorker magazine runs a cartoon caption contest where readers are invited to submit a caption for a cartoon. And each week, Bob Mankoff, cartoon editor of the magazine, and his staff sort through thousands of submissions to find the funniest entry. To speed this process up and further engage readers, Mankoff and the New Yorker enlisted the help of NEXT, a research project that uses crowdsourced data and active learning algorithms, to help choose the winner.

Bob Mankoff explains how NEXT has changed how the winner is chosen:

Help choose this week’s winner:
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NEXT is a cloud based machine learning system I developed in my PhD  work at the University of Wisconsin – Madison with Prof. Rob Nowak and Lalit Jain that I continue to work on in my postdoc in the AMP lab. The adaptive data-collection algorithms in NEXT decide which New Yorker cartoon captions to ask participants to judge based on the observation that even after a small number of judgments, there are some captions that are clearly not funny. Consequently, our algorithms automatically stop requesting judgments for the unpromising entries and focus on trying out the ones that might get a laugh. With active learning algorithms like this, the winner can be determined from far fewer total judgments and with greater certainty than using standard crowdsourcing methods that collect an equal number of judgments for every caption (regardless of how good or bad).

To learn more about NEXT and how it can help with your application, visit our project page: