Game of Tropes: 

Exploring the Placebo Effect in Computational Creativity 

Tony Veale 

School of Computer Science and Informatics 
University College Dublin, Belfield D4, Ireland. 
Tony.Veale@UCD.ie 


Abstract 

Twitter has proven itself a rich and varied source oflanguage data for linguistic analysis. For Twitter is 
more than a popular new channel for social interactionin language; in many ways it constitutes a whole newgenre 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 anopportunity of another kind to computationally-mindedresearchers of language, a generative opportunity to 
study how algorithmic systems might exploit linguistictropes to compose novel, concise and re-tweetable textsof their own. This paper evaluates one such system, aTwitterbot named @MetaphorMagnet that packages itsown 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. 

Tropes: 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 ill-
suited 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 ill-
fitting 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 oflinguistic containers, the more incentive one has to exploitor 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 conveyboth their meaning and their attitude to this meaning. So 
each micro-text, or tweet, becomes more than a containerof 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 metaphorto build this aesthetic and thus build up a loyal audienceof followers for their world view. Yet Twitter challengesmany 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 ofa linguistic container to its contents. Failure to do so canhave serious repercussions when one is communicating tothousands of followers at once, with tweets that demandconcision and leave little room for nuance. It is thus not 
unusual for even creative tweets to come packaged withan 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 fully-

Proceedings of the Sixth International Conference on Computational Creativity June 2015 


autonomous computational agent – a Twitterbot – that 
crafts its own metaphorical and ironical tweets from itsown knowledge-base of common-sense facts and beliefs.
How might such a system exhibit a sense of irony thathuman users will find worthy of attention, and how mightthis system craft interesting metaphoric insights from aknowledge-base of everyday facts that are as banal asthey are uncontentious? We shall explore the variety oflinguistic containers at the disposal of this agent – a realcomputational 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 non-
obvious combination of familiar linguistic containers with 
unsurprising factual fillers. In support of this view, weshall 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 physicalcontainer by its shape, one can often guess the meaning ofa linguistic container by its form. We become habituatedto familiar containers, and just as we might imagine ourown 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 inferthe presence of meaning in texts that are well-formed andseemingly the product of an intelligent entity, even if thisentity is not intelligent and any meaning is not intentional. 
Remarkably, Twitter shows that we willingly extend thisgenerosity of interpretation to the outputs of bots that weknow to be unthinking users of wholly aleatoric methods.
Twitterbots exploit this placebo effect – wherein a well-
formed linguistic container is presumed to convey a well-
founded semantic content – by serving up linguistic formsthat readers tacitly fill with their own meanings. We aimto empirically demonstrate here that readers do more thanwillingly suspend their disbelief, and that a well-packaged 
linguistic form can seduce readers into seeing what is notthere: a comprehensible meaning, or at least an intent tobe meaningful. We do this by evaluating two metaphor-
generating bots side-by-side: a rational, knowledge-based 
Twitterbot named @MetaphorMagnet vs. an aleatoric and 
largely knowledge-free bot named @MetaphorMinute. 

Digital Surrealists: La Regle 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 surrealist writers – 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 regle 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 et al. (2012) 
also uses a template-guided version of the cut-up method 
to mash together semantically-coherent text fragments in 
a way that – much like @Pentametron – obeys certain 
over-arching constraints on metre and rhyme. These text 
fragments come from a variety of online sources, ranging 
from short tweets to long news articles. News stories are a 
rich source of readymade phrases that convey resonant 
images, and these can be clipped from a news text using 
standard NLP techniques, while tweets that use affect-rich 
language can also be extracted automatically via standard 
sentiment analysis lexica and tools. Thus, a large stock of 
resonant similes, such as “blue as a blueberry” or “hot as 
a sauna” can be extracted from the Web using a search 
engine (Veale, 2014), since the simile frame “as X as Y” 
is specific enough to query for, and promiscuous enough 
to match, a rich diversity of typical X:Y associations. 
These associations can then be recast in a variety of poetic 
forms to make their clichéd offerings seem fresh again, as 
in “Blueberry-blue overalls” or “sauna-hot jungle.” 

Proceedings of the Sixth International Conference on Computational Creativity June 2015 


Indeed, the very act of juxtaposing clichés can itself be 
a creative act, as evidenced both by the success of the cutup 
method in general and that of specific cut-ups in 
particular. Consider William Empson’s withering analysis 
of the persnickety, cliché-hating George Orwell, whom 
Empson called “the eagle eye with the flat feet” (quoted in 
Ricks [1995:356], who admires Empson’s “audacious 
compacting of clichés”). The Full-FACE system is just 
one of many CC systems that use an autonomous variant 
of Burroughs and Gysin’s cut-up method to integrate tight 
constraints on form with loose constraints on meaning. 

Breton famously stated that “Je ne veux pas changer la 
regle du jeu, je veux changer de jeu.” Twitterbots do not 
change or transcend their own rules, but different bots do 
represent different language games with their own rules. 
So to change the game, a CC developer can simply build a 
new bot, to exploit a different set of tropes and linguistic 
containers. It is rare for any one Twitterbot to incorporate 
a diverse set of tropes and production mechanisms; each 
typically follows Breton’s experimentalist approach to art 
in its random sampling of a specific space of possibilities. 
Each bot thus forms its own art installation, to showcase a 
single generative idea. @MetaphorMagnet, the bot at the 
heart of this paper, represents a departure from this norm, 
insofar as it exploits a wide range of tropes and rendering 
strategies, it employs diverse sources of knowledge, and it 
applies a variety of reasoning styles to generate surprising 
conclusions from what is otherwise a stock of banal facts. 
But does this added sophistication – bought at the cost of 
increased system complexity and knowledge-engineering 
effort – result in tweets that are seen as more meaningful, 
novel, apt or retweetable by human users? It is this point 
that exercises us most in the coming sections. 

The Placebo Effect : Trope-A-Dope 

We humans obtain more mileage than we care to admitfrom templates, tropes and other “bot” tricks for linguisticcreativity. Consider what Matthew McGlone and Jessica 
Tofighbakhsh (1999) call the Keats heuristic, an insight 
into creative language use that owes as much to Nietzsche(“we sometimes consider an idea truer simply because ithas a metrical form and presents itself with a divine skip 
and jump”) as to the poet John Keats (“Beauty is truth, 
truth beauty”). McGlone and Tofighbakhsh (2000) showthat when presented with uncommon maxims or proverbswith internal rhyme (e.g. “woes unite foes”), subjects tendto view these as more insightful about the world than theequivalent paraphrases with no internal rhyme at all (e.g.
“troubles unite enemies”). While the Keats heuristic is not 
exactly a license to pun, it is an incentive to rhyme, and togive as much weight (or more still) to superficial aspectsof poetry generation as to deep semantics and pragmatics. 
Indeed, the heuristic is tacitly central to the operation ofvirtually every computational creativity (CC) approach topoetry generation (e.g. Milic, 1970; Chamberlain & Etter,
1983; Gervás, 2000; Manurung et al. 2012; Veale, 2013).
If human poets ask questions first and rhyme later, CCsystems typically rhyme first and ask questions later, if at 

all. For if the human jury in the O.J. Simpson trial couldbe turned against bald facts with the Keatsian “If the glove 
don’t fit you must acquit”, readers of computer-generated 
poetry can be persuaded to see deliberate meaning and 
resonance in any output that has a “divine skip and jump.” 

There is something undeniably special about poetry, 
whether it is the gentle poetry of William Shakespeare’s 
“Shall I compare thee to a summer’s day” or the rough 
poetry of Johnnie Cochrane’s “If the glove don’t fit you 
must acquit”. Milic (1970), an early CC pioneer, argues 
that while poetry “is more difficult to write than prose” it 
offers other freedoms to writers due to the willingness of 
readers to “interpret a poem, no matter how obscure, until 
he has achieved a satisfactory understanding.” What then 
of the enigmatic tweets of bots like @MetaphorMinute, 
whose obscurity is a function of random word choice and 
whose surface forms are not designed to make any sense 
at all? Milic argues that computer poetry serves a useful 
role other than its obviously generative one, by alerting us 
to “the curious behavior of familiar words in unfamiliar 
combinations.” Behaviour that makes perfect sense when 
dealing with the writings of a gifted human poet, such as 
our tendency to “interpret an utterance by making what 
concessions are necessary on the assumption that a writer 
has something in mind of which the utterance is the sign”, 
is, argues Milic, “inappropriate when the speaker is a 
computer.” Yet Twitterbots benefit from such concessions 
and assumptions whether or not followers know them to 
be bots. This placebo effect is especially pronounced in 
the coining of would-be metaphors, leading Milic to note 
“how readily we accept metaphor as an alternative to 
calling a sentence nonsensical.” @MetaphorMinute and 
other aleatoric bots wring maximal value from this insight 
by devising texts that they themselves cannot distinguish 
from nonsense. So this begs an important question: are the 
meanings imposed on a random text by a creative human 
of comparable value to those conveyed by a bot with its 
own model of the world and its own insights to tweet? 

Building Metaphors : Theory and Practice 

What might it mean for a bot to have “something in mind 
of which [its] utterance is the sign”? When it comes to 
metaphor generation, we might expect that our bot would 
generate its figurative tweets from a conceptual model of 
the world as it sees it, in a way that accords with a sound 
theory of how and why humans actually use metaphor. For 
the latter, AI offers us a range of models to choose from. 

Computational approaches to metaphor divide into four 
broad classes: the categorial, the corrective. the analogical 
and the schematic. Categorial approaches view metaphor 
as a means to reconceptualize one idea by placing it into a 
taxonomic category strongly associated with another (see 
Hutton, 1982; Way, 1991; Glucksberg, 1998). Corrective 
approaches view metaphor as an inherently anomalous 
deviation from literal language, and strive to recover the 
corresponding literal meaning of any figurative statement 
that violates its lexico-semantic norms (see Wilks, 1978; 

Proceedings of the Sixth International Conference on Computational Creativity June 2015 


Fass, 1991). The analogical approaches aim to capture the 
relational parallels that allow our representation of an idea 
in one domain, the source, to be systematically projected 
onto our mental representation of an idea in another, the 
target (see Gentner et al., 1989; Veale and Keane, 1997). 
Finally, schematic approaches aim to explain how related 
linguistic metaphors arise as surface manifestions of deep 
seated cognitive structures called Conceptual Metaphors 
(Lakoff & Johnson, 1980; Carbonell, 1981; Martin, 1990; 
Veale & Keane, 1992). Each approach has its own merits, 
but none offers a complete computational solution. Bots 
that aim for a general competence in metaphor must thus 
implement a selective hybrid of multiple approaches. Yet 
each approach also requires its own source of knowledge. 
Categorial approaches require a comprehensive taxonomy 
of flexible categories that can embrace atypical members 
on demand. Corrective approaches are built on a substrate 
of literal case-frames onto which deviant usages can be 
correctively projected. Analogical approaches assume an 
inventory of graph-theoretic representations of concepts, 
from which a structure-mapping engine can eke out its 
sub-graph isomorphisms. Schematic approaches rely on a 
stock of Conceptual Metaphors (CMs) – such as Life is a 
Journey or Theories are Buildings – to unearth the deep 
structures beneath the surface of diverse linguistic forms. 

Though hybrid approaches demand multiple sources of 
knowledge, there exist public Web services that integrate 
this knowledge with the appropriate means of using it for 
metaphor. The Thesaurus Rex Web service of Veale & Li 
(2013) provides a highly divergent system of fine-grained 
categorizations that allows a 3rd-party client system to e.g. 
determine that War and Divorce have each been viewed 
as kinds of destructive thing, traumatic event and severe 
conflict in the texts of the Web. The Metaphor Eyes Web 
service of Veale & Li (2011) is a rich source of relational 
norms – also harvested at scale from Web texts – such as 
that businesses earn profits and pay taxes, or that religions 
ban alcohol and believe in reincarnation. The Metaphor 
Magnet service of Veale (2014) offers a rich source of the 
stereotypical properties and behaviors of familiar ideas, 
and provides the means to retrieve salient CMs from the 
Google n-grams (Brants & Franz, 2006) which can then 
be further elaborated to create novel linguistic metaphors. 

@MetaphorMagnet relies on each of these public Web 
services to generate the conceptual conceits that underpin 
its figurative tweets. For instance, it uses Thesaurus Rex 
to provide the categorization insights that it then packages 
as odd-one-out lists or as faux-dictionary definitions. It 
uses the Metaphor Eyes service to provide the relational 
structures it needs to perform structure mapping and thus 
concoct original analogies and dis-analogies. And it uses 
the Metaphor Magnet service to access the stereotypical 
properties and behaviors of ideas, and to juxtapose these 
properties via resonant contrasts and norm contraventions. 
Once the conceptual chassis of a metaphor is constructed 
in this way, it is then packaged in an apt linguistic form. 

Building Strings: Trope-On-A-Rope 

CMs such as Life Is A Journey and Politics Is A Game are 
more than productive deep-structures for the generation of 
whole families of linguistic metaphors; they also provide 
the conceptual mappings that shape our habitual thinking 
about such familiar ideas as Life, Love, Politics and War. 

Politicians and philosophers exploit conceptual metaphorsto frame an issue and shape our expectations; when a CMfails to match our own experience, we reject it and switchto a more apt metaphor. So a metaphor-generating bot canthus create a thought-provoking opposition by pitting oneCM against another that advocates a conflicting view ofthe world. The following tweet from @MetaphorMagnetuses this approach to contrast two views on #Democracy: 

To some voters, democracy is an important cornerstone.
To others, it is a worthless failure. 
#Democracy=#Cornerstone #Democracy=#Failure 

The CM Democracy Is A Cornerstone (of society) is oftenused to frame political discussions, and can be seen as an 
specialization of the CM Society Is A Building, itself an 
elaboration of the CM Organization Is Physical Structure(see Grady, 1997). Yet the importance of cornerstones tothe buildings they anchor finds a sharp contrast in theassertion that Democracy Is A Failure. Each of these 
affective claims is so commonly asserted that they can be 
found in the Google n-grams, a large database of shortfragments of frequent Web texts. The 4-gram “democracy 
is a cornerstone” has a frequency of 91 in the Google n-
grams, while the 4-gram “democracy is a failure” has a 
frequency of 165. These n-grams, which suggest potentialCMs for @MetaphorMagnet, are elaborated with added 
detail via the Metaphor Magnet Web service, which tells 
the bot that the stereotypical cornerstone is important and 
the stereotypical failure is worthless. The following tweetmakes similar use of CMs found in the Google n-grams,
but renders the conflict in a different linguistic container: 

Remember when tolerance was promoted by crusadingliberals? Now, tolerance is violence that only fearful 
appeasers can avoid. 

The bot is guided here by the suggestive Google 3-gram“Tolerance for Violence” (frequency=1353), but it doesnot directly contrast the ideas #Tolerance and #Violence.
Instead, it finds a potential analogy in this juxtaposition,
between the promoters of #Tolerance (which it renders ascrusading liberals) and the opponents of #Violence 
(which it renders as fearful appeasers). The choice of 
stereotypical properties (crusading and fearful) is drivenby the bot’s need to create a resonant semantic opposition.
The bot omits the hashtags #Tolerance=#Violence fromthis tweet due to the confines of Twitter’s 140-character 
limit. But it can also choose to render a complex conceitacross two successive tweets, as in the following pair: 

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Remember when research was conducted by prestigious 
philosophers? #Research=#Fruit #Philosopher=#Insect 

Now, research is a fruit eaten only by lowly insects. 
#Research=#Fruit #Philosopher=#Insect 

@MetaphorMagnet uses a number of packaging strategiesto turn a figurative comparison into an ironic observation, 
ranging from the use of an explicit #irony hashtag (which 
is commonplace on Twitter) to the use of “scare” quotesto focus on the part of a tweet most deserving of disbelief.
The following tweet showcases both of these strategies: 

#Irony: When some chefs prepare "fresh" salads the way 
apothecaries prepare noxious poisons.

#Chef=#Apothecary #Salad=#Poison 

Irony offers a concise means of contrasting two points ofview: that which is expected and the disappointing reality.
By comparing the preparation of salads – the “healthy” 
option on most menus – to the preparion of poisons, thisanalogy undermines the expectation of healthfulness and 
suggests that some salads are noxious and chemical-filled.
The real world is filled with situations in which naturally 
antagonistic properties are found in surprising proximity.
These situations, if expressed in the right linguistic form,
can be elevated to the level of situational irony. Consider,
for instance, the following @MetaphorMagnet tweet: 

#Irony: When the timers that are found in enjoyablegames activate gruesome bombs. #Enjoyable=#Gruesome 

It is important to stress that @MetaphorMagnet does not 
simply fill linguistic templates with related words. Rather,
the above tweet is constructed at the knowledge-level, bya bot that intentionally seeks out stereotypical norms thatare related (e.g. by a pivotal idea timer) yet which can beplaced into antagonistic juxtapositions around this pivot.
In effect, the goal of the linguistic rendering is to packagea knowledge-level conceit – typically a conflict of ideasand properties – in a tweet-sized narrative. For example,
the following tweet is rendered as a narrative of change: 

To join and travel in a pack: This can turn pretty girlsinto ugly coyotes. #Girl=#Coyote 

Twitter offers unique social affordances that allow a 
bot to elevate almost any contrast of ideas into a dramaticnarrative. Rather than talk of generic liberals or appeasers,
a bot can give these straw men real names, or at leastinvent fake names that look like the real thing and which,
as Twitter handles, seem wittily apropos to the views that 
are espoused. In this way, by imagining its central conceitas a topic of a vigorous debate by real people, a bot canturn an abstract metaphor into a concrete situation with itsown colorful participants. Consider the social debate that 
is made personal in this tweet from @MetaphorMagnet: 

.@war_poet says history is a straight line 
.@war_prisoner says it is a coiled chain 
#History=#Line #History=#Chain 

The handles @war_poet and @war_prisoner are invented 

by @MetaphorMagnet to suit, and amplify, the figurativeviews that they are advanced in the tweet, by using a mix 
of relational knowledge (from the Metaphor Eyes service)
and language data (via the Google n-grams). Since poetswrite poems about the wars that punctuate history, andpoems contain lines, the 2-gram “war poet” is recognizedas an apt handle for an imaginary Twitter user who mightadvance a view of history as a line. In this case the handle 
@war_poet really does name a real Twitter user, but thisonly adds to the sense that Twitterbot confections are anew kind of interactive theatre and performance art (seeDewey, 2014). Note that the more profound aspects ofthis contrast are not appreciated by @MetaphorMagnetitself, or at least not yet. For example, the bot does not yet 
appreciate what it means for history to be a straight line,
and while it knows enough to invent the intriguing handle@war_prisoner, neither does it appreciate what it might 
mean to be a prisoner of history, enslaved in a repeatingcycle of war. The placebo effect is not a binary effect: it 
benefits by degrees, and can benefit knowledge-rich botsjust as much as knowledge-free bots. Our bots will alwaysevoke in we humans more than they themselves can everappreciate, yet this may itself be a key part of CC’s allure. 

Bot Vs. Bot : The Metaphor Challenge 

@MetaphorMagnet differs from @MetaphorMinute in a 
number of key ways. For one, its mechanics are informed 
by Lakoff and Johnson’s Conceptual Metaphor Theoryand a range of computational approaches. For another, itdraws on considerable semantic and linguistic resources,
from a large knowledge-base of conceptual relations and 
stereotypical beliefs to the linguistic diversity of the 
Google n-grams. All of @MetaphorMagnet’s tweets – all 
its hits and all its misses – are open to public scrutiny onTwitter. But to empirically evaluate the success of the botas a knowledge-based, theory-driven producer of novel,
meaningful and retweet-worthy metaphors, we turn to thecrowdsourcing platform CrowdFlower, where we conduct 
a comparative evaluation of @MetaphorMagnet and its 
closest knowledge-free counterpart, @MetaphorMinute. 
The latter, designed by noted bot-maker Darius Kazemi,
uses a wholly aleatoric approach to metaphor generationyet has over 500 followers on Twitter that do not mind itsone-every-two-minutes scattergun approach to generation. 
@MetaphorMinute crafts metaphors by filling a template 
with nouns and adjectives that are chosen more-or-less at 
random, to produce inscrutable tweets such as “a cubit is 
a headboard: stational yet tongue-obsessed.” 

We chose 60 tweets at random from the past outputs of 
each Twitterbot. CrowdFlower annotators, who were each 
paid a small sum per judgment, were not informed of the 
origin of any tweet, but simply told that each was selected 
from Twitter because of its metaphorical content. We did 
not want annotators to actively suspend their disbelief by 
knowingly dealing with bot outputs. Annotators were paid 
to rate the content of each tweet along three dimensions, 

Proceedings of the Sixth International Conference on Computational Creativity June 2015 


Comprehensibility, Novelty and likely Retweetability, and 
to rate all three dimensions on the same scale: Very Low 
to Medium Low to Medium High to Very High. Ten 
annotations were solicited for each dimension of each 
tweet, though the responses of likely scammers (non-
engaged annotators) were later removed from the dataset. 
Tables 1 through 3 present the distributions of mean 
ratings per tweet, for each dimension and each Twitterbot. 

Comprehensibility @Metaphor 
Magnet 
@Metaphor 
Minute 
Very Low 
Med. Low 
11.6% 
13.2% 
23.9% 
22.2% 
Med High 23.7% 22.4% 
Very High 51.5% 31.6% 

Table 1. Relative Comprehensibility of each bot 

So more than half of @MetaphorMagnet’s tweets were 
ranked as having very high comprehensibility, while less 
than one third of @MetaphorMinute’s tweets are so 
ranked. More surprising, perhaps. is the result that 
annotators found more than half of @MetaphorMinute’s 
wholly random metaphors to have medium-high to very-
high comprehensibility. This Twitterbot’s use of abstruse 
terminology, such as stational and peachblow, may be a 
factor here, as might the bot’s use of the familiar copula 
container X is Y for its metaphors, which may well seduce 
annotators into believing that an apparent metaphor really 
does have a comprehensible meaning, if only one were to 
expend enough mental energy to actually discern it. 

Tweet Novelty @Metaphor @Metaphor 
Magnet Minute 
Very Low 11.9% 9.5% 
Med. Low 17.3% 12.4% 
Med High 21% 14.9% 
Very High 49.8% 63.2% 

Table 2. Comparative Novelty of each bot’s tweets 

The dimension Novelty yields results that are equally 
surprising. While half of @MetaphorMagnet’s metaphors 
are rated as having very-high novelty in Table 2, almost 
two-thirds of @MetaphorMinute’s tweets are just as 
highly rated. However, we should not be overly surprised 
that @MetaphorMinute’s bizarre juxtapositions of rare or 
unusual words, as yielded by its unconstrained use of 
aleatoric techniques, are seen as more unusual than those 
word juxtapositions arising from @MetaphorMagnet’s 
controlled use of attested Web n-grams and stereotypical 

knowledge. As shown by Giora et al. (2004), novelty is 
neither a source of pleasure in itself nor is it a reliable 
benchmark of creativity. Rather, pleasurability derives 
from the recognition of useful novelty, that is, novelty that 
can be understood and appreciated relative to the familiar. 

Re-Tweetability @Metaphor 
Magnet 
@Metaphor 
Minute 
Very Low 
Med. Low 
15.5% 
41.9% 
41% 
34.1% 
Med High 27.4% 15% 
Very High 15.3% 9.9% 

Table 3. Relative Retweetability of each bot’s tweets 

On Twitter, useful exploitation is frequently a matter of 
social reach. A tweet is novel and useful to the extent that 
it attracts the attention of Twitter users and is deemed 
worthy of re-tweeting to others in one’s social circle. Our 
third dimension, Re-Tweetability, reflects the likelihood 
that an annotator would ever consider re-tweeting a given 
metaphorical tweet to others. Though we ask annotators 
to speculate here – neither bot has enough followers to 
perform a robust statistical analysis of actual retweet rates 

– the results largely conform to our expectations. The 
results of Table 3 show retweetability to be a matter of 
novelty and comprehensibility, and not just novelty alone. 
Though annotators are not generous with their Very-High 
ratings for either bot, @MetaphorMagnet’s tweets are 
judged to be considerably more re-tweetable than the 
largely random offerings of @MetaphorMinute. 
Comprehensibility and comprehension are two different 
things: while a Computational Creativity (CC) version of 
the placebo effect may well foster a belief that a given 
tweet has a coherent meaning, it cannot actually provide 
this meaning. Meaning is the product of interpretation, 
and interpretation is often hard. Milic (1970) notes that in 
a context that licences a poetic interpretation, such as one 
in which a reader is told that a particular text is a 
metaphor, readers are more likely to accept that the text – 
as inscrutable as it may be – has a metaphorical meaning 
rather than dismiss it as nonsense. Recall that over 75% of 
@MetaphorMagnet’s tweets and over 50% of 
@MetaphorMinute’s tweets are judged as having 
medium-high to very-high comprehensibility. We thus 
need to look deeper, to determine whether raters can 
actually back up these judgments with actual meanings. 

In a second CrowdFlower experiment, we make raters 
work harder, to reconstruct a partial tweet by adding the 
missing information that will make it whole and apt again. 
That is, we employ a cloze test format for this experiment, 
by removing from each tweet the pair of key qualities that 
anchor the tweet and make its comparison of ideas seem 
meaningful and apt. For @MetaphorMagnet, for example, 
we remove the properties detailed and vague in this tweet: 

Proceedings of the Sixth International Conference on Computational Creativity June 2015 


To some freedom fighters, freedom is a detailed recipe. To 
others, it is a vague dream. #Freedom=#Recipe 
#Freedom=#Dream 

For @MetaphorMinute, we excise the pair of qualities 
hippy and revisional from the following tweet: 

a flatfoot is a houseboat: hippy and revisional 

For each tweet from each bot, we blank out a pair of 
original qualities as above; this pairing is the answer that 
is sought from human judges. We also choose 4 distractor 
pairs for each original pair, by selecting pairs from other 
tweets from the same bot. As in our first experiment, we 
chose 60 tweets at random from the past outputs of each 
bot, and 10 ratings were solicited for each. Annotators 
were presented with a tweet in which the key properties 
were blanked out (as above), and given five randomly 
ordered pairs of possible fillers to choose from. To make 
the results of the experiment comparable to those of the 
1st experiment (Tables 1,2,3), we obtain the mean aptness 
of each tweet, so that e.g. if 7 out of 10 raters correctly 
choose the original pairing, then that tweet is deemed to 
have an aptness of 0.7. We then place these aptness scores 
into bands, where the Very Low band = 0 to 0.25, Medium 
Low = 0.26 to 0.5, Medium High = 0.51 to 0.75, and Very 
High = 0.76 to 1. By calculating the distribution of tweets 
to each band, we can determine e.g. the percentage of 
tweets from each bot that are put into the Very High band. 

Our hypothesis is rather straightforward: if tweets are 
linguistic containers that are carefully crafted to convey a 
particular meaning, then it should be easier to select the 
missing pair of qualities that make this meaning whole; if, 
on the other hand, the tweet is all there is, and its content 
is chosen mostly at random, then raters will choose the 
right pairing with no more success than random selection. 

The results reported in Table 4 bear out our hypothesis. 

Metaphor Aptness @Metaphor @Metaphor 
Magnet Minute 

Very Low 0% 84% 
Med. Low 22% 16% 
Med High 58% 0% 
Very High 20% 0% 


Table 4. Relative Aptness of each bot’s metaphors 

The placebo effect in CC can lead us to appreciate a bot’s 
tweets as meaningful but cannot tell us what this meaning 
should be. Though the results above may seem a foregone 
conclusion, as @MetaphorMagnet’s tweets are designed 
to communicate a fully recoverable meaning while those 
of @MetaphorMinute are not, this is surely what it means 
to engage in real communication: to design an utterance 
so that an intended meaning is re-created, in whole or in 
part, in the mind of an intelligent, receptive audience. 

Fake It ‘Til You Make It 

The Placebo Effect benefits all Computational Creativity 
systems, from superficial users of surealistic techniques to 
sophisticated knowledge-based AI systems. That this is so 
should come as no surprise, for we humans also benefit 
from the effects of an active and receptive mind when 
dealing with other people. Just as a prior belief in the 
efficacy of a medical intervention can lead us to perceive 
(and experience) a post-hoc benefit from an otherwise 
empty treatment, a prior belief in the meaningfulness of a 
verbal intervention can lead us to perceive (and enjoy) a 
creative meaning where none was ever intended. When a 
CC system uses superficial techniques to convey a sense 
of understanding and profundity with otherwise shallow 
linguistic forms, as in Weizenbaum’s (1965) infamous 
ELIZA system, the label “ELIZA Effect” proves to be an 
apt one (Hofstadter, 1995). However, we humans are also 
subject to an ELIZA effect of our own, insofar as we often 
do others the courtesy of assuming their utterances to be 
freighted with real meaning and creative intent, and will 
often work hard to uncover that meaning for them. 

At one time or another, we have all relied on catchphrases, 
clichés, slogans, idioms, canned jokes and other 
half-empty linguistic containers to suggest to others that 
we have deeper meanings in mind, or have something 
more profound to offer, than we actually do. In a famous 
polemical essay from 1946, George Orwell excoriates 
speakers of English for their reliance on jargon, foreign 
words and empty phraseology as a substitute for thoughts 
of real substance, while Geoff Pullum (2003) upbraids 
modern speakers for a grating over-reliance on “multi-use, 
customizable, instantly recognizable, time-worn, quoted 
or misquoted phrases or sentences that can be used in an 
entirely open array of different jokey variants by lazy 
journalists and writers.” These “phrases for lazy writers 
in kit form” are not that different from the template-based 
language games played by superficial Twitterbots, and 
though we humans fill our templates – such as “X is the 
new black”, “In X no one can hear you scream” or “if the 
Eskimos have N words for snow then Xs surely have as 
many for Y” – with lexical fillers that are contextually apt, 
we employ our templates to be just as provocative, and to 
imply or to suggest more than we actually mean. 

To see machines work with humans in the construction 
of real figurative meanings, readers are directed to a 
variant of @MetaphorMagnet – a related bot named 
@MetaphorMirror – that tweets its own novel metaphors 
in response to breaking news events. This bot’s metaphors 
are not offered as informative summaries of the news, but 
as figurative lenses through which followers can view the 
news and adopt a novel perspective on human affairs. 

Acknowledgements 

This research was supported by the EC project WHIM: 

Proceedings of the Sixth International Conference on Computational Creativity June 2015 


The What-If Machine. See http://www.whim-project.eu/ 
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