The pair are behind a prototype tool that applies natural language processing, information theory, and network science to allow readers to explore how Charles Dickens created and managed his complex world of characters.
Adam, a lecturer in the School of English, Film, Theatre, and Media Studies, says that while traditional literary scholarship is based on interpretation of the language of a text as well as its socio-cultural context, computational analysis offers new ways of capturing structural dimensions of texts that were previously inaccessible.
“The novels of Charles Dickens are particularly interesting within this field of research. Not only was Dickens a central figure in the development of the nineteenth-century novel, but his narratives construct vast and elaborate character networks representing the rapidly changing Victorian world,” he says.
Dickens was a pioneer of the serial novel form, writing monthly or weekly instalments of his novels over the course of up to 19 months. His character networks are important because they capture complex social relationships.
Markus, a senior lecturer in the School of Information Management, acknowledges that the disciplines of English Literature and Network Science might appear to be incompatible, but says, “We’re not abandoning literary interpretation; we’re looking at what happens when these two elements are brought together and have found that they mutually inform each other.
“In most of Dickens’s novels, characters are introduced gradually over the first half, and you can see that very clearly in our analysis with the tool prototype. In two of his later novels—Our Mutual Friend and Little Dorrit—all the characters are introduced in the first eighth of the story, so the graphs look very different from his early novels. This visually supports the findings of other Dickens scholars,” says Markus.
Adam admits they soon discovered that using computational methodology to track characters can be a difficult process. “Dickens didn’t name all of his characters, so using analytics fell down there,” he says. However, these limitations have produced their own insights, with Adam and one of the team’s research assistants, Isabel Parker, now publishing an essay highlighting the importance of anonymous characters in Dickens’s novels.
The tool has been tested on 19 novels—all 15 of Dickens’s novels and four by other Victorian novelists for comparative purposes.
An initial user study to evaluate the tool was undertaken involving English literature scholars and university students. The study showed how the tool can help readers see important structural elements of a novel such as how characters in different social classes are kept separate in Great Expectations.
Do Markus and Adam have great expectations for future applications of their tool?
“We’re excited about what else it will reveal in literary texts. We’re now working with Professor Ronald Fischer in the School of Psychology to adapt our tool, so that instead of simply tracking characters, we can see how texts present human personality traits through time,” says Adam.