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Data-Realism : Reading and Writing Datafied Text. / Erslev, Malthe Stavning; Pold, Søren Bro.
In: Electronic Book Review, 2020.Research output: Contribution to journal/Conference contribution in journal/Contribution to newspaper › Journal article › Research › peer-review
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TY - JOUR
T1 - Data-Realism
T2 - Reading and Writing Datafied Text
AU - Erslev, Malthe Stavning
AU - Pold, Søren Bro
PY - 2020
Y1 - 2020
N2 - In this forward-looking and somewhat tentative call for a realism of data - or a data-realism - we chart out three literary experiments all relating to one of the most pressing aspects of the contemporary conditions of writing, namely the role of data-driven support software including machine learning. Questioning how to read the text’s data - or the data’s text - we develop our approach in dialogue with three works of e-lit, arguing that these works present three distinct, yet related, ways of engaging with the underlying data-infrastructures of computerized language. Thus, we read these three works of e-lit in order to examine (1) how predictive keyboards change the notion of who is writing (or texting) whom in Erica Scourti’s THINK YOU KNOW ME; (2) how Google’s AdWords algorithm creates and manages a linguistic capitalism as investigated by Pip Thornton’s {poem}.py; and (3) how the style of generative text is becoming both recognizable and re-mixable, forming a machine learning rhetoric, evident twitter user @KeatonPatti’s viral “Olive Garden Tweet.” Refraining from presenting a closed conclusion, we instead speculate the relevance of our data-realist approach by presenting an e-lit meditation of the concept, taking the form of the latest iteration of The Poetry Machine: “The Oracle from Selphie.”
AB - In this forward-looking and somewhat tentative call for a realism of data - or a data-realism - we chart out three literary experiments all relating to one of the most pressing aspects of the contemporary conditions of writing, namely the role of data-driven support software including machine learning. Questioning how to read the text’s data - or the data’s text - we develop our approach in dialogue with three works of e-lit, arguing that these works present three distinct, yet related, ways of engaging with the underlying data-infrastructures of computerized language. Thus, we read these three works of e-lit in order to examine (1) how predictive keyboards change the notion of who is writing (or texting) whom in Erica Scourti’s THINK YOU KNOW ME; (2) how Google’s AdWords algorithm creates and manages a linguistic capitalism as investigated by Pip Thornton’s {poem}.py; and (3) how the style of generative text is becoming both recognizable and re-mixable, forming a machine learning rhetoric, evident twitter user @KeatonPatti’s viral “Olive Garden Tweet.” Refraining from presenting a closed conclusion, we instead speculate the relevance of our data-realist approach by presenting an e-lit meditation of the concept, taking the form of the latest iteration of The Poetry Machine: “The Oracle from Selphie.”
KW - realism
KW - data aesthetics
KW - Electronic literature
U2 - 10.7273/n381-mk15
DO - 10.7273/n381-mk15
M3 - Journal article
JO - Electronic Book Review
JF - Electronic Book Review
SN - 1553-1139
ER -