Game of Tropes: Exploring the Placebo Effect in Computational Creativity

Twitter has proven itself a rich and varied source of language data for linguistic analysis. For Twitter is more than a popular new channel for social interaction in language; in many ways it constitutes a whole new genre of text, as users adapt to its new limitations (140 character messages) and to its novel conventions such as retweeting and hash-tagging. But Twitter presents an opportunity of another kind to computationally-minded researchers of language, a generative opportunity to study how algorithmic systems might exploit linguistic tropes to compose novel, concise and re-tweetable texts of their own. This paper evaluates one such system, a Twitterbot named @MetaphorMagnet that packages its own metaphors and ironic observations as pithy tweets. Moreover, we use @MetaphorMagnet, and the idea of Twitterbots more generally, to explore the relationship of linguistic containers to their contents, to understand the extent to which human readers fill these containers with their own meanings, to see meaning in the outputs of generative systems where none was ever intended. We evaluate this placebo effect by asking human raters to judge the comprehensibility, novelty and aptness of texts tweeted by simple and sophisticated Twitterbots.  Containers of Meaning A mismatch between a container and its contents can often tell us much more than the content itself, as when a person places the ashes of a deceased relative in a coffee can, or sends a brutal death threat in a Hallmark greeting card. The communicative effectiveness of mismatched containers is just one more reason to be skeptical of the Conduit metaphor (Reddy, 1979) – which views linguistic constructs as containers of propositional content to be faithfully shuttled between speaker and hearer – as a realistic model of human communication. Language involves more than the faithful transmission of logical propositions between information-hungry agents, and more effective communication – of attitude, expectation and creative intent – can often be achieved by abusing our linguistic containers of meaning than by treating them with the sincerity that the Conduit metaphor assumes. Consider the case of verbal irony, in which a speaker deliberately chooses containers that are pragmatically illsuited to the conveyance of their contents. For instance, the advertising container “If you only see one [X] this year, make it this one” assumes that [X] denotes a category of event – such as “romantic comedy” or “movie about superheroes” – with a surfeit of available members for a listener to choose from. When [X] is bound to the phrase “comedy about Anne Frank” or “musical about Nazis”, the container proves too hollow for its content, and the reader is signaled to the presence of playful irony. Though such a film may well be one-of-a-kind, the illfitting container suggests there are good reasons for this singularity that do not speak to X’s quality as an artistic event. Yet if carefully chosen, an apparently inappropriate container can communicate a great deal about a speaker’s relationship to the content conveyed within, and as much again about the speaker’s relationship to their audience. As more practical limitations are placed on the form of linguistic containers, the more incentive one has to exploit or abuse containers for creative ends. Consider the use of Twitter as a communicative medium: writers are limited to micro-texts of no more than 140 characters to convey both their meaning and their attitude to this meaning. So each micro-text, or tweet, becomes more than a container of propositional content: each is a brick in a larger edifice that comprises the writer’s online personae and textual aesthetic. Many Twitter users employ irony and metaphor to build this aesthetic and thus build up a loyal audience of followers for their world view. Yet Twitter challenges many of our assumptions about irony and metaphor. Such devices must be carefully modulated if an audience is to perceive a speaker’s meaning in the playful (mis)match of a linguistic container to its contents. Failure to do so can have serious repercussions when one is communicating to thousands of followers at once, with tweets that demand concision and leave little room for nuance. It is thus not unusual for even creative tweets to come packaged with an explicit tag such as #irony, #sarcasm or #metaphor. Metaphor and irony are much-analysed phenomena in social media, but this paper takes a generative approach, to consider the production rather than the analysis of creative linguistic phenomena in the context of a fullyautonomous computational agent – a Twitterbot – that crafts its own metaphorical and ironical tweets from its own knowledge-base of common-sense facts and beliefs. How might such a system exhibit a sense of irony that human users will find worthy of attention, and how might this system craft interesting metaphoric insights from a knowledge-base of everyday facts that are as banal as they are uncontentious? We shall explore the variety of linguistic containers at the disposal of this agent – a real computational system named @MetaphorMagnet – to better understand how such containers can be playfully exploited to convey ironic, witty or thought-provoking views on the world. With @MetaphorMagnet we aim to show that interesting messages are not crafted from interesting contents, or at least not necessarily so. Rather, effective tweets emerge from an appropriate if nonobvious combination of familiar linguistic containers with unsurprising factual fillers. In support of this view, we shall present an empirical analysis of the assessment of @MetaphorMagnet’s uncurated outputs by human judges. Just as one can often guess the contents of a physical container by its shape, one can often guess the meaning of a linguistic container by its form. We become habituated to familiar containers, and just as we might imagine our own uses for a physical container, we often pour our own meanings into suggestive textual forms. For in language, meaning follows form, and readers will generously infer the presence of meaning in texts that are well-formed and seemingly the product of an intelligent entity, even if this entity is not intelligent and any meaning is not intentional. Remarkably, Twitter shows that we willingly extend this generosity of interpretation to the outputs of bots that we know to be unthinking users of wholly aleatoric methods. Twitterbots exploit this placebo effect – wherein a wellformed linguistic container is presumed to convey a wellfounded semantic content – by serving up linguistic forms that readers tacitly fill with their own meanings. We aim to empirically demonstrate here that readers do more than willingly suspend their disbelief, and that a well-packaged linguistic form can seduce readers into seeing what it not there: a comprehensible meaning, or at least an intent to be meaningful. We do this by evaluating two metaphorgenerating bots side-by-side: a rational, knowledge-based Twitterbot named @MetaphorMagnet vs. an aleatoric and largely knowledge-free bot named @MetaphorMinute. Digital Surrealists: La Règle Du Jeu Most Twitterbots are simple, rule-based systems that use stochastic methods to explore a loosely-defined space of texual forms. Such bots are high-concept, low-complexity text-production mechanisms that transplant the aleatoric techniques of the surrealist poets – from André Breton to William Burroughs and Brion Gysin – into the realms of digital content, social networking and online publishing. Each embodies a language game with its own generative rules, or what Breton called “la règle du jeu.” Yet Breton, Burroughs and Gysin viewed the use of aleatorical rules as merely the first stage of a two-stage creation process: at this first stage, random recombinant methods are used to confect candidate texts in ways that, though unguided by meaning, are also free of the baleful influence of cliché; at the second stage, these candidates are carefully filtered by a human, to select those that are novel and interesting. Most bots implement the first stage and ignore the second, pushing the task of critiquing and filtering candidate texts onto the humans who read and selectively re-tweet them. Nonetheless, some bots achieve surprising effects with the simplest language tools. Consider @Pentametron, a bot that generates accidental poetry by re-tweeting pairs of random tweets of ten syllables apiece (for an iambic pentameter reading) if each ends on a rhyming syllable. When the meaning of each tweet in a couplet coheres with the other, as in “Pathetic people are everywhere” |“Your web-site sucks, @RyanAir”, the sum of tweets produces an emergent meaning that is richer and more resonant than that of either tweet alone. Trending social events such as the Oscars or the Super Bowl are especially conducive to just this kind of synchronicity, as in this fortuitous pairing: “So far the @SuperBowl commercials blow.” | “Not even gonna watch the halftime show.” In contrast, a bot named @MetaphorMinute wears its aleatoric methods on its sleeve, for its tweets – such as “a haiku is a tonsil: peachblow yet snail-paced” – are not so much random metaphors as random metaphor-shaped texts. Using a strategy that stresses quantity over quality, this bot instantiates that standard linguistic container for metaphors – the copula frame “X is a Y” – with mostly random word choices every two minutes. Interestingly, its tweets are as likely to provoke a sense of mystification and ersatz profundity as they are total incomprehension. Yet bots such as @Pentametron and @MetaphorMinute do not generate their texts from the semantic-level up; rather, they manipulate texts at the word-level, and thus lack any sense of the meaning of a tweet, or any rationale for why one tweet might be better – which is to say, more interesting, more apt or more re-tweetable – than others. The Full-FACE poetry generator of Colton