Effects of Semantic Relatedness between Setups and Punchlines in Twitter Hashtag Games

Andrew Cattle, Xiaojuan Ma


Abstract
This paper explores humour recognition for Twitter-based hashtag games. Given their popularity, frequency, and relatively formulaic nature, these games make a good target for computational humour research and can leverage Twitter likes and retweets as humour judgments. In this work, we use pair-wise relative humour judgments to examine several measures of semantic relatedness between setups and punchlines on a hashtag game corpus we collected and annotated. Results show that perplexity, Normalized Google Distance, and free-word association-based features are all useful in identifying “funnier” hashtag game responses. In fact, we provide empirical evidence that funnier punchlines tend to be more obscure, although more obscure punchlines are not necessarily rated funnier. Furthermore, the asymmetric nature of free-word association features allows us to see that while punchlines should be harder to predict given a setup, they should also be relatively easy to understand in context.
Anthology ID:
W16-4308
Volume:
Proceedings of the Workshop on Computational Modeling of People’s Opinions, Personality, and Emotions in Social Media (PEOPLES)
Month:
December
Year:
2016
Address:
Osaka, Japan
Venues:
PEOPLES | WS
SIG:
Publisher:
The COLING 2016 Organizing Committee
Note:
Pages:
70–79
Language:
URL:
https://www.aclweb.org/anthology/W16-4308
DOI:
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PDF:
http://aclanthology.lst.uni-saarland.de/W16-4308.pdf