At face value this bot seeks solutions to what many call “the crisis of the Humanities” by offering “tips on how to stop the crisis in the humanities. Real solutions!” Its operation is conceptually straightforward: it completes a sentence template that begins with “To save the humanities, we need to” and then completes the sentence, I imagine with the results of a search in Twitter for tweets that contain “we need to” or “we must.” This creates grammatically correct sentences that offer solutions that vary in their fit or appropriateness. For example:
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.
This bot takes Tweet-sized snippets of text from movie reviews aggregated in Rotten Tomatoes, identifies nouns in the subject position, and replaces those with the names of right-wing pundits who appear regularly on the Fox News Channel, attaching the ironically intended hashtag #PraiseFOX. The bot was created essentially as joke for the politically charged comedy show The Colbert Report, as a reaction to the news that right-wing media had staff dedicated to refuting anything threatening to their ideological point of view, as explained by Stephen Colbert in the clip below.
These two bots generate responses to questions that have such subjective answers that no number of responses can really satisfy anyone, but do so in thought-provoking and amusing fashion.
“Is it art?” explores the challenge to the art world posed by the readymade Dada sculpture “Fountain,” attributed to Marcel Duchamp. His gesture of sending a standard urinal to be displayed in galleries as an art object, with a title and signed “R. Mutt” was very controversial and provoked questions about the nature of art. This bot is on an endless rant on the artistic or not artistic nature of different things, making statement such as:
Am I the only one who thinks transactions are not art?
— Is it art? (@IsItArtBot) June 12, 2014
This bot is “@thricedotted’s twittercat,” a virtual pet that interacts with them and its followers by doing the things cats do. Sometimes it meows or purrs, sometimes it describes actions, such as “*leaves dissected animals on the front step*” and
*gets into trouble* =^.^=
— glitch[FETA] ~(=^‥^) (@storyofglitch) June 12, 2014
These tweets occur on a seemingly random timer, but you can always get a reaction by interacting with it. For example, if you follow it on Twitter, it will follow you. If you address it, it responds.
This bot is “brute-forcing an episode from [Thomas Pynchon’s novel] Gravity’s Rainbow” by tweeting the words “you never did the Kenosha kid” with different punctuation every two hours. The bot description links to a Language Log entry that explains the episode– basically about a man who, under the effects of sodium amytal, goes on “an obsessive meditation on alternative possible analyses of the six-word sequence ‘you never did the kenosha kid.'” Inspired by the algorithm described here, Darius Kazemi created a bot that seeks all the possible combinations of that word sequence with punctuation (and appropriate capitalization). The result is a tour-de-brute-force of different syntactic structures and meanings that can emerge from this simple string of words. Try reading the following tweets out loud.
This bot tirelessly carries out a task too large for it to complete within a human lifetime: it explores an idea posed by Jorge Luis Borges in his story “The Library of Babel” of an infinite library full of books that contain a different combination of 23 letters and punctuation marks. “Each book contains four hundred ten pages; each page, forty lines; each line, approximately eighty black letters” (Schneider quotes Borges in the bot’s description). With this bot, Schneider illustrates the concept of this library via Twitter’s own constraints by tweeting 140 characters randomly chosen from 23 alphabetic characters, punctuation marks, and spaces. The result is pure language noise. . . or is it?
This bot randomly tweets suggestions sent to it based on a simple constraint: two 4-letter words to be tattooed onto the knuckles of the hands and juxtaposed. The resulting tweets show both versatility and imagination– and is a popular creative constraint in tattoo circles, as we can see in collections such as this one. By tweeting the words in uppercase letters, it focuses on the language of the tattoos, de-emphasizing potential graphical information.
— Knuckle Tat (@knuckle_tat) June 8, 2014
Visit this link to read its most popular tweets.
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.
- @tokiale – This bot clones @everyword but in Toki Pona.
- @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
— southpaw (@nycsouthpaw) June 7, 2014
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!