Learning to Evaluate Humor in Memes Based on the Incongruity Theory

Kohtaro Tanaka, Hiroaki Yamane, Yusuke Mori, Yusuke Mukuta, Tatsuya Harada

Abstract
Memes are a widely used means of communication on social media platforms, and are known for their ability to “go viral”. In prior works, researchers have aimed to develop an AI system to understand humor in memes. However, existing methods are limited by the reliability and consistency of the annotations in the dataset used to train the underlying models. Moreover, they do not explicitly take advantage of the incongruity between images and their captions, which is known to be an important element of humor in memes. In this study, we first gathered real-valued humor annotations of 7,500 memes through a crowdwork platform. Based on this data, we propose a refinement process to extract memes that are not influenced by interpersonal differences in the perception of humor and a method designed to extract and utilize incongruities between images and captions. The results of an experimental comparison with models using vision and language pretraining models show that our proposed approach outperformed other models in a binary classification task of evaluating whether a given meme was humorous.
Anthology ID:
2022.cai-1.9
Volume:
Proceedings of the Second Workshop on When Creative AI Meets Conversational AI
Month:
October
Year:
2022
Address:
Gyeongju, Republic of Korea
Editors:
Xianchao Wu, Peiying Ruan, Sheng Li, Yi Dong
Venue:
CAI
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
81–93
Language:
URL:
https://aclanthology.org/2022.cai-1.9
DOI:
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Cite (ACL):
Kohtaro Tanaka, Hiroaki Yamane, Yusuke Mori, Yusuke Mukuta, and Tatsuya Harada. 2022. Learning to Evaluate Humor in Memes Based on the Incongruity Theory. In Proceedings of the Second Workshop on When Creative AI Meets Conversational AI, pages 81–93, Gyeongju, Republic of Korea. Association for Computational Linguistics.
Cite (Informal):
Learning to Evaluate Humor in Memes Based on the Incongruity Theory (Tanaka et al., CAI 2022)
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