Make America Grate Again: Artist Statement

In June 2015, Donald Trump descended down the escalator at Trump Tower—flanked by his wife Melania and a sea of passionate fans in white t-shirts—to launch his candidacy to be the next President of the United States. Despite the never-ending debate among political pundits as to whether Trump’s victory can be attributed to racial resentment or economic anxiety in the American electorate: Trump himself minced no words on that fateful Tuesday, famously attacking Mexican immigrants as “rapists” and “criminals” while painting a racially-tinged picture of America as a nation ridden by “crime and gangs and drugs.”

I used Tracery to re-generate the following lines from Donald Trump’s first speech—from when he announced his candidacy for President of the United States—to produce a large number of combinations of words to demonstrate the rambling and nonsensical nature of his speeches in Make America Grate Again:

“When Mexico sends its people, they’re not sending their best. They’re not sending you. … They’re bringing drugs. They’re bringing crime. They’re rapists. And some, I assume, are good people.”

Make America Grate Again is inspired by Donald Trump’s Presidential Announcement Speech. Data comes from the list of living things and the list of 200 Creepy Verbs. Made by Preksha Agarwal with Tracery.

 

Trump partly generated such fervent support as a candidate—famously (and accurately) boasting that he could “stand in the middle of 5th Avenue and shoot somebody and I wouldn’t lose voters”—because his speeches were spontaneous and full of surprises, especially compared to that of his groomed opponent, Hillary Clinton. A Trump speech is a stream-of-consciousness adventure that can be difficult to follow, but is always engaging; for example, Trump would initiate chants of “lock her up,” before quickly pivoting to some falsehood about Muslims and immigrants. Trump, of course, isn’t a politician—his emergence in the national conscience stems from his role as the host of the reality show The Apprentice. Candidate Trump’s talent as an entertainer manifests through his intentional use of spontaneity and surprise to excite crowds and captive viewers. Yet, while the introduction of spontaneity can be a useful tool to generate novel, unpredictable results in art and literature, in politics that same usage can be dangerous. Trump supporters might have viewed his speeches as pure entertainment, but now that he is president, his words have huge real-world consequences. (Just ask the Muslims impacted by Trump’s travel ban, or the ambitious dreamers now in-limbo by looming changes to DACA.) Donald Trump highlights the potential of randomness in political rhetoric to galvanize voters and lead to massive political and social changes—as Trump’s victory surely has. Trump’s rambling speeches, starting with his campaign announcement two years ago, exploit the fears and insecurities of Americans by using spontaneous asides that hit at the core of their racist and xenophobic tendencies.

In “Death of the Author,” Barthes explains the relation between the narrator and its characters “by making the narrator not he who has seen and felt nor even he who is writing, but he who is going to write” (Barthes 144). I would like to extend this relation to Trump’s speeches and the power it gives to those with pre-existing racist sentiments to intensify their hatred of certain groups of people. When Trump delivers his speeches, he is the author, but as soon as that moment passes, “the modern scriptor” is born (Barthes 145). His audience take on the role of what Barthes defines as the “modern scriptor,” and are no longer confined to the views and ideas of the “Author” (Barthes 145). The danger of Trump’s racially-tinged speeches is that that they can motivate hate and racism; in fact, hate crimes increased 20 percent in 2016, and the number of hate groups increased last year, with a tripling in the number of anti-Muslim groups from 2015. Just as Barthes argues that “writing ceaselessly posits meaning ceaselessly to evaporate it”, Make America Grate Again refuses to assign an ultimate meaning to this text, liberating us from the need to decipher meaning or anything truly revolutionary in his speeches.

Donald Trump uses words and phrases to tap into the fears and insecurities of the American people in a way that could very well be like the “drawing words out of a hat” technique of textual production as explained in Burroughs’s “The Cut-Up Method of Byron Gysin” (Burroughs 89-91). Burroughs convincingly argues that “the best writing seems to be done almost by accident” (Burroughs 90) and that great literature can be created by introducing unpredictable spontaneous factors in the creative process (Burroughs 90). The cut-up method of random-action can be used to our advantage in games and military strategy (Burroughs 91), but Trump’s speeches illustrate the opposite situation in which the use of spontaneity and surprise has dangerous consequences.

The point of this? Masterman summarizes the superiority of using computer-generated texts in producing an indefinitely large combination of words in poetry and dialogue (Masterman 36). I would like to advance this argument to show that even a computer can be taught to “damp down” the permutational resources of a language to generate tolerable sentences. These “grammatically correct but semantically randomised sentences” are a commentary on our President’s ability to create dialogue with his audience (Masterman 36). Trump’s path to victory in 2016 hinged on spontaneous asides in his campaign speeches that frequently appealed to the dark impulses of the American electorate. This computer-generated text can create the same randomness and entertainment of the most captivating Trump speech, without the damaging real-world consequences.

 

Work Cited:

Barthes, Roland. “The Death of the Author”. Image-Music-Text. 1967, p. 143-148.

Burroughs, William S. “The Cut-Up Method of Brion Gysin”. The New Media Reader, The MIT Press, Cambridge, MA, 2003, p. 89–91.

Masterman, Margaret. “The Use of Computers to Make Semantic Models of Language”. Astronauts of Inner-Space. 1966. P. 36-38.

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