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“Authorship attribution is helpful if you suspect fraud: for instance, if you believe that Shakespeare wasn’t educated enough to write the plays, or that Charlotte Brontë’s Jane Eyre was really written by her brother, Branwell. It’s also helpful if authorship is unknown and you want to assign credit, say, for the Neapolitan Novels of “Elena Ferrante”—or if you want to know whom to blame. In the example I discuss below, the Romantic poet-philosopher-critic Samuel Taylor Coleridge wanted very much to know who blasted his 1816 volume, Christabel; Kubla Khan, a Vision; The Pains of Sleep, in the Edinburgh Review. Though Coleridge had opinions about who wrote the review, no one in the last two hundred years has been able to identify that person with certainty.
The basic question of authorship attribution—who wrote what—is a question we’re increasingly able to answer, using computers that employ stylistic analysis. But this process also creates new questions about the role of the author: not about whether it’s the author or reader who makes meaning from a text, but what it means to write something at all.
In some naive algorithmic approaches that know nothing of the author—or, indeed, of history, ideology, or critical traditions—authorship emerges powerfully from the sea of texts as a set of shared patterns. That is, an artificial intelligence, or AI, successfully “recognizes” an author not as a person, but instead as the likeness of features that characterize a body of work. In order to find patterns across texts, the algorithmic “reader” uses a collection of textual traits—like frequently used words or punctuation—to draw conclusions about who wrote what.”
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