Don't Take it Personally: Analyzing Gender and Age Differences in Ratings of Online Humor

TL;DR

This study examines how gender and age influence perceptions of humor and offense in online ratings, revealing differences in responses and understanding that impact computational humor systems.

Contribution

It provides new insights into how gender and age affect humor and offense ratings, highlighting the importance of modeling subjectivity in humor detection.

Findings

Women rate humor lower and offense higher than men.

The correlation between humor and offense increases with age.

No gender or age differences in humor detection accuracy.

Abstract

Computational humor detection systems rarely model the subjectivity of humor responses, or consider alternative reactions to humor - namely offense. We analyzed a large dataset of humor and offense ratings by male and female annotators of different age groups. We find that women link these two concepts more strongly than men, and they tend to give lower humor ratings and higher offense scores. We also find that the correlation between humor and offense increases with age. Although there were no gender or age differences in humor detection, women and older annotators signalled that they did not understand joke texts more often than men. We discuss implications for computational humor detection and downstream tasks.