How Did This Get Funded?! Automatically Identifying Quirky Scientific Achievements
This work addresses the challenge of humor mining in computational settings, which is an AI-complete problem, by introducing a novel task for automatically identifying quirky scientific achievements.
The paper tackles the problem of automatically detecting funny and unusual scientific papers, inspired by the Ig Nobel prize, by constructing a dataset of thousands of funny papers and learning classifiers that combine psychology, linguistics, and NLP. The results demonstrate the potential of these methods to identify potentially funny papers in a dataset of over 630,000 articles.
Humor is an important social phenomenon, serving complex social and psychological functions. However, despite being studied for millennia humor is computationally not well understood, often considered an AI-complete problem. In this work, we introduce a novel setting in humor mining: automatically detecting funny and unusual scientific papers. We are inspired by the Ig Nobel prize, a satirical prize awarded annually to celebrate funny scientific achievements (example past winner: "Are cows more likely to lie down the longer they stand?"). This challenging task has unique characteristics that make it particularly suitable for automatic learning. We construct a dataset containing thousands of funny papers and use it to learn classifiers, combining findings from psychology and linguistics with recent advances in NLP. We use our models to identify potentially funny papers in a large dataset of over 630,000 articles. The results demonstrate the potential of our methods, and more broadly the utility of integrating state-of-the-art NLP methods with insights from more traditional disciplines.