Estos dos bots se basan en el concepto de los “snowclones”, que es un fenómeno lingüístico mejor descrito por Erin O’Connor en su maravilloso blog y recurso “The Snowclones Database“.
Un snowclone es un tipo particular de cliché, originado popularmente por Geoff Pullum. El nombre proviene del muy difamado Dr. Pullum “Si los esquimales tienen N palabras para nieve, X seguramente tiene Y palabras para Z”. Un ejemplo más fácil podría ser “X es la nueva Y.” La breve definición de este neologismo podría ser n. rellenar el encabezado en blanco.
Complete las frases nemotécnicas en blanco? Esto está maduro para un tratamiento bot.
These two bots are based on the concept of snowclones, which are a linguistic phenomenon best described by Erin O’Connor in her wonderful blog and resource “The Snowclones Database.”
A snowclone is a particular kind of cliche, popularly originated by Geoff Pullum. The name comes from Dr. Pullum’s much-maligned “If Eskimos have N words for snow, X surely have Y words for Z”. An easier example might be “X is the new Y.” The short definition of this neologism might be n. fill-in-the-blank headline.
Fill in the blank mnemonic phrases? This is ripe for a bot treatment.
The three bots reviewed in this entry all carry out essentially the same technique– they create a tweet based on the juxtaposition of material from two different sources– yet produce output that feels quite different. The reasons for this are partly thematic, partly due to the data source, and partly because of the way the join the juxtaposed elements.
An important early bot that uses this technique is Ranjit Bhatnagar’s @Pentametron, which retweets iambic pentameter tweets joined by end rhyme and creating surprisingly cohesive and occasionally humorous couplets. Juxtaposition is also a poetic technique that became prominent with Modernism and is a central strategy in Ezra Pound’s poetry and poetics. This entry will analyze “Two Headlines” by Darius Kazemi, “Dreams, juxtaposed” by Allison Parrish, and “And Now Imagine” by Ivy Baumgarten.
To celebrate Allison Parrish’s achievement– getting her bot @everyword to complete its 7 year tour-de-force of tweeting every word in the English language in alphabetical order, every 30 minutes– this entry will briefly examine 12 bots inspired and followed by @everyword. If you’ve never heard of this, you may want to read this earlier entry in which I analyzed the bot from an e-poetic perspective. Here are some comments on the bots, in the order they appear in the list of bots followed by @everyword.
@fuckeveryword – Every good work deserves a worthy parody. This bot mimics @everyword in every way, but adds “fuck” before each word. It must have a shorter dictionary, because it will be done fucking the English language by 2017.
@everybrendan – This bot is supposedly “twittering every Brendan name in Project Gutenberg” but I’m not sure how that produces the output it tweets. (Update: it’s created by Leonard Richardson and documented here -thanks for the heads up, Tully)I suspect it’s as profoundly weird as this other project by Brendan Atkins.
@everyletter – With a data set of 26 letters, this self explanatory bot completed its mission in about 3 minutes. It has 142 followers and has been retweeted and favorited extensively.
@everycolorbot – This bot by Colin Bayer is tweets hourly a randomly selected color from the RGB color spectrum, which contains 16,777,216 different colors. It is a wonderful way to discover colors that we may not have precise names for, and it is developing an enthusiastic following.
@languagepix – operates like @everyword, but also tweets the first image it finds on a Google Image search for that word. The word and picture pairings are generally illustrative, often surprising, and occasionally absurd.
@everyarabicword – This bot implements @everyword in Arabic and should complete its task in 2019.
ALL LEMMATA (@eveywilliwaw) – This bot by Liam Cooke already tweeted all 2600 words “consisting only of straight lines.” What a wonderful graphical constraint!
@PowerVocabTweet – Allison Parrish describes this bot as “a procedural exploration in a genre I like to call ‘speculative lexicography’—basically, @everyword‘s dada cousin.” Follow it to enhance your vocabulary with nonsense words with plausible definitions.
@everyunicode – Ramsey Nasser’s bot gives the @everyword treatment to every character in the Unicode 6.2 standard, which contains 1,114,112 characters and should take 63 years to complete. For a compressed expression of a similar context, see Jörg Piringer’s Unicode video.
@defineeveryword – This bot by Mike Dory bravely attempted to define every word tweeted by @everyword until it broke on “urinalysis” on February 21, 2014.
@iederwoord – John Schop’s Dutch version of @everyword.
There is something irresistible about a project with a clear beginning and an ending because we can build a narrative around it. As I write this entry, @everyword is tweeting away its last few words and every single one of them is retweeted, favorited, and replied to dozens of times. The excitement and suspense on what will be the last word is palpable and people are drawing connections between the word and the bot’s context.
You're ending with our beginnings, word. 🙁 RT @everyword: zygotic
But more important than the excitement of the moment is the inspiration that this simple bot has offered in carrying out its absurd, celebrated task. You know you’re on to something when you’re imitated, remixed, parodied, and extended.
Congratulations to Allison Parrish and @everyword for completing its task and thank you for the inspiration!
This bot mines the Twitter stream for phrases starting with “when,” extracts the clauses, and joins each phrase with a randomly selected animated GIF in a Tumblr. Here’s a more detailed description from Parrish’s blog:
A “#whatshouldwecallme-style tumblr” is one in which animated GIFs are paired with a title expressing a circumstance or mood—usually a clause beginning with “when.” I wrote a Python script to make these kinds of posts automatically. Here’s what it does:
(1) Search Twitter for tweets containing the word “when.”
(2) Extract the “when” clause from such tweets.
(3) Use Pattern to identify “when” clauses with suitable syntax (i.e., clauses in which a subject directly follows “when”; plus some other heuristic fudging)
(4) Post the “when” clause as the title of a tumblr post, along with an animated GIF randomly chosen from the imgur gallery.
This bot has been on deceptively simple mission since it was launched in 2007: it is tweeting the English language, one word every 30 minutes, in alphabetical order. This work of conceptual poetry is delightfully absurd because it claims to be “twittering every word” and even offers a termination date in which such a project would be complete— when even the concept of what constitutes the English language is subject to debate, even if it wasn’t changing on a daily basis. To make such a feat even possible (unless you’re Wowbagger The Infinitely Prolonged) requires setting constraints—such as a choice of dictionary— though it is to Parrish’s credit that she doesn’t disclose the source, because it enhances the project’s conceptual claim.