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        <title>Effects of Semantic Relatedness between Setups and Punchlines in Twitter Hashtag Games</title>
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    <name type="personal">
        <namePart type="given">Andrew</namePart>
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    <name type="personal">
        <namePart type="given">Xiaojuan</namePart>
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            <title>Proceedings of the Workshop on Computational Modeling of People’s Opinions, Personality, and Emotions in Social Media (PEOPLES)</title>
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        <name type="personal">
            <namePart type="given">Malvina</namePart>
            <namePart type="family">Nissim</namePart>
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        <name type="personal">
            <namePart type="given">Viviana</namePart>
            <namePart type="family">Patti</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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            <namePart type="given">Barbara</namePart>
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    <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.</abstract>
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%0 Conference Proceedings
%T Effects of Semantic Relatedness between Setups and Punchlines in Twitter Hashtag Games
%A Cattle, Andrew
%A Ma, Xiaojuan
%Y Nissim, Malvina
%Y Patti, Viviana
%Y Plank, Barbara
%S Proceedings of the Workshop on Computational Modeling of People’s Opinions, Personality, and Emotions in Social Media (PEOPLES)
%D 2016
%8 December
%I The COLING 2016 Organizing Committee
%C Osaka, Japan
%F cattle-ma-2016-effects
%X 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.
%U https://aclanthology.org/W16-4308
%P 70-79

Markdown (Informal)

[Effects of Semantic Relatedness between Setups and Punchlines in Twitter Hashtag Games](https://aclanthology.org/W16-4308) (Cattle & Ma, PEOPLES 2016)

• Effects of Semantic Relatedness between Setups and Punchlines in Twitter Hashtag Games (Cattle & Ma, PEOPLES 2016)

ACL

• Andrew Cattle and Xiaojuan Ma. 2016. Effects of Semantic Relatedness between Setups and Punchlines in Twitter Hashtag Games. In Proceedings of the Workshop on Computational Modeling of People’s Opinions, Personality, and Emotions in Social Media (PEOPLES), pages 70–79, Osaka, Japan. The COLING 2016 Organizing Committee.