An Algorithm Can Tell Us How Much Shakespeare Was Actually Written by Shakespeare

There’s no shortage of Shakespeare conspiracy theories out there. But unlike that one kid in every sixth grade class reading A Midsummer Night’s Dream who raises their hand to say, “I heard Shakespeare didn’t even, like, write his plays,” modern literary analysts are getting highly scientific with their theories, and they’ve got the math to back them up.

The most recent innovation in Shakespeare conspiracy theory mining comes in the form of machine-learning algorithms that can identify and track Shakespeare’s style, distinguishing it from the work of other writers who may have had an unsung hand in the bard’s famous texts.

According to MIT Technology Review, Petr Plecháč at the Czech Academy of Sciences in Prague has reportedly applied this technique to Shakespeare’s Henry VIII, which literary analysts have long speculated was co-authored by fellow English playwright John Fletcher. The theory first gained mainstream attention in the literary community when it was posed by analyst James Spedding in 1850, but now Plecháč claims his algorithm can identify exactly where Shakespeare’s contributions end and Fletcher’s begin.

“Our results highly support the canonical division of the play between William Shakespeare and John Fletcher proposed by James Spedding,” said Plecháč.

By training the algorithm to recognize Shakespeare’s and Fletcher’s unique styles using examples of their work, the machine-learning technique can then mine Henry VIII for each writer’s specific contributions to the text. The results ultimately suggested that Fletcher was responsible for roughly half of the play, even pinpointing exact lines within a single scene where one author took over for the other.

Plecháč also trained his model to recognize the work of Philip Massinger, another Shakespeare contemporary who is thought to have been Henry VIII‘s co-author, but ultimately found little evidence to support the theory.

Rumors about Shakespeare’s authorship have swirled for centuries, and by applying technology and machine learning to his work, it seems we’re closer than ever to having real answers. You had a good run, Billy, but the nerds are onto you.

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