  In this work, we present a new dataset for computational humor, specifically
comparative humor ranking, which attempts to eschew the ubiquitous binary
approach to humor detection. The dataset consists of tweets that are humorous
responses to a given hashtag. We describe the motivation for this new dataset,
as well as the collection process, which includes a description of our
semi-automated system for data collection. We also present initial experiments
for this dataset using both unsupervised and supervised approaches. Our best
supervised system achieved 63.7% accuracy, suggesting that this task is much
more difficult than comparable humor detection tasks. Initial experiments
indicate that a character-level model is more suitable for this task than a
token-level model, likely due to a large amount of puns that can be captured by
a character-level model.
