Cards Against AI: Predicting Humor in a Fill-in-the-blank Party Game

TL;DR

This paper introduces a large dataset of humor from Cards Against Humanity, analyzes it, and develops models to predict winning jokes, revealing insights into humor's social aspects and model focus.

Contribution

It provides a novel dataset of 300,000 game instances, analyzes humor patterns, and trains models that outperform random guessing in predicting humorous punchlines.

Findings

Models achieve 20% accuracy in predicting winners.

Punchline cards are the main focus of the models.

Juvenile and crude punchlines tend to win more often.

Abstract

Humor is an inherently social phenomenon, with humorous utterances shaped by what is socially and culturally accepted. Understanding humor is an important NLP challenge, with many applications to human-computer interactions. In this work we explore humor in the context of Cards Against Humanity -- a party game where players complete fill-in-the-blank statements using cards that can be offensive or politically incorrect. We introduce a novel dataset of 300,000 online games of Cards Against Humanity, including 785K unique jokes, analyze it and provide insights. We trained machine learning models to predict the winning joke per game, achieving performance twice as good (20\%) as random, even without any user information. On the more difficult task of judging novel cards, we see the models' ability to generalize is moderate. Interestingly, we find that our models are primarily focused on punchline card, with the context having little impact. Analyzing feature importance, we observe that short, crude, juvenile punchlines tend to win.