  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.
