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While historically computational humor paid very little attention to sociology and mostly took into account subparts of linguistics and some psychology, Christie Davies wrote a number of papers that should affect the study of computational humor directly. This paper will look at one paper to illustrate this point, namely Christie’s chapter in the Primer of Humor Research.  With the advancements in computational processing and big data analysis/analytics, it is becoming possible to look at a large collection of humorous texts that are available on the web. In particular, older texts, including joke materials, that are being scanned from previously published printed versions. Most of the approaches within computational humor concentrated on comparison of present/existing jokes, without taking into account classes of jokes that are absent in a given setting. While the absence of a class is unlikely to affect classification – something that researchers in computational humor seem to be interested in – it does come into light when features of various classes are compared and conclusions are being made. This paper will describe existing approaches and how they could be enhanced, thanks to Davies’s contributions and the advancements in data processing.

Computational humor and Christie Davies’ basis for joke comparison