research-article

Inside Jokes: Identifying Humorous Cartoon Captions

Authors:

Robert MankoffAuthors Info & Claims

KDD '15: Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

Pages 1065 - 1074

https://doi.org/10.1145/2783258.2783388

Published: 10 August 2015 Publication History

Abstract

Humor is an integral aspect of the human experience. Motivated by the prospect of creating computational models of humor, we study the influence of the language of cartoon captions on the perceived humorousness of the cartoons. Our studies are based on a large corpus of crowdsourced cartoon captions that were submitted to a contest hosted by the New Yorker. Having access to thousands of captions submitted for the same image allows us to analyze the breadth of responses of people to the same visual stimulus.

We first describe how we acquire judgments about the humorousness of different captions. Then, we detail the construction of a corpus where captions deemed funnier are paired with less-funny captions for the same cartoon. We analyze the caption pairs and find significant differences between the funnier and less-funny captions. Next, we build a classifier to identify funnier captions automatically. Given two captions and a cartoon, our classifier picks the funnier one 69% of the time for captions hinging on the same joke, and 64% of the time for any pair of captions. Finally, we use the classifier to find the best captions and study how its predictions could be used to significantly reduce the load on the cartoon contest's judges.

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Cited By

Kariyawasam HNiwarthana APalmer AKay JWithana A(2024)Appropriate Incongruity Driven Human-AI Collaborative Tool to Assist Novices in Humorous Content GenerationProceedings of the 29th International Conference on Intelligent User Interfaces10.1145/3640543.3645161(650-659)Online publication date: 18-Mar-2024
https://dl.acm.org/doi/10.1145/3640543.3645161
Zhong SHuang ZGao SWen WLin LZitnik MZhou P(2024)Let's Think Outside the Box: Exploring Leap-of-Thought in Large Language Models with Creative Humor Generation2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)10.1109/CVPR52733.2024.01258(13246-13257)Online publication date: 16-Jun-2024
https://doi.org/10.1109/CVPR52733.2024.01258
Kalloniatis AAdamidis P(2024)Computational humor recognition: a systematic literature reviewArtificial Intelligence Review10.1007/s10462-024-11043-358:2Online publication date: 20-Dec-2024
https://doi.org/10.1007/s10462-024-11043-3
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Index Terms

Inside Jokes: Identifying Humorous Cartoon Captions

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Published In

cover image ACM Conferences

KDD '15: Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

August 2015

2378 pages

ISBN:9781450336642

DOI:10.1145/2783258

General Chairs:
Longbing Cao
University of Technology, Sydney
,
Chengqi Zhang
University of Technology, Sydney
,
Program Chairs:
Thorsten Joachims
Cornell University
,
Geoff Webb
Monash University
,
Dragos D. Margineantu
Boeing Research
,
Graham Williams
Australian Taxation Office

Copyright © 2015 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 10 August 2015

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KDD '15

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KDD '15: The 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

August 10 - 13, 2015

NSW, Sydney, Australia

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KDD '15 Paper Acceptance Rate 160 of 819 submissions, 20%;

Overall Acceptance Rate 1,133 of 8,635 submissions, 13%

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Cited By

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https://dl.acm.org/doi/10.1145/3640543.3645161
Zhong SHuang ZGao SWen WLin LZitnik MZhou P(2024)Let's Think Outside the Box: Exploring Leap-of-Thought in Large Language Models with Creative Humor Generation2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)10.1109/CVPR52733.2024.01258(13246-13257)Online publication date: 16-Jun-2024
https://doi.org/10.1109/CVPR52733.2024.01258
Kalloniatis AAdamidis P(2024)Computational humor recognition: a systematic literature reviewArtificial Intelligence Review10.1007/s10462-024-11043-358:2Online publication date: 20-Dec-2024
https://doi.org/10.1007/s10462-024-11043-3
Rosenbusch HVisser T(2023)Humor appreciation can be predicted with machine learning techniquesScientific Reports10.1038/s41598-023-45935-113:1Online publication date: 3-Nov-2023
https://doi.org/10.1038/s41598-023-45935-1
Veale T(2023)Great Expectations and EPIC Fails: A Computational Perspective on Irony and SarcasmThe Cambridge Handbook of Irony and Thought10.1017/9781108974004.017(216-234)Online publication date: 20-Dec-2023
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Shani CShahaf D(2022)Can Computers Understand Humor?Frontiers for Young Minds10.3389/frym.2022.98260410Online publication date: 24-Oct-2022
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Rosenbusch HEvans AZeelenberg M(2022)The Relative Importance of Joke and Audience Characteristics in Eliciting AmusementPsychological Science10.1177/0956797622109859533:9(1386-1394)Online publication date: 24-Aug-2022
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