"President Vows to Cut <Taxes> Hair": Dataset and Analysis of Creative Text Editing for Humorous Headlines

TL;DR

This paper introduces Humicroedit, a dataset of news headlines with humorous edits, analyzes humor theories, and develops classifiers to predict humor, advancing computational humor research.

Contribution

It provides a new dataset with annotated humorous edits, supporting humor theory analysis and baseline models for automatic humorous headline generation.

Findings

Data supports classic humor theories like incongruity and superiority.

Baseline classifiers can predict humor in edited headlines.

The dataset enables future research in computational humor.

Abstract

We introduce, release, and analyze a new dataset, called Humicroedit, for research in computational humor. Our publicly available data consists of regular English news headlines paired with versions of the same headlines that contain simple replacement edits designed to make them funny. We carefully curated crowdsourced editors to create funny headlines and judges to score a to a total of 15,095 edited headlines, with five judges per headline. The simple edits, usually just a single word replacement, mean we can apply straightforward analysis techniques to determine what makes our edited headlines humorous. We show how the data support classic theories of humor, such as incongruity, superiority, and setup/punchline. Finally, we develop baseline classifiers that can predict whether or not an edited headline is funny, which is a first step toward automatically generating humorous headlines as an approach to creating topical humor.