Modeling Modernist Dialogism
Close Reading with Big Data
Bibliographic Data
| ID | 20057236 |
|---|---|
| Authors | Adam Hammond (0000-0002-7422-4336, San Diego State University, corresponding author), Julian Brooke (The University of Melbourne), Graeme Hirst (0000-0001-9482-1042, University of Toronto) |
| Year | 2016 |
| Pages | 49-77 |
| Publication date | 2016-01-01 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | CHAPTER |
| Venue | Reading Modernism with Machines (SOURCE_BOOK) |
| Publisher | Palgrave Macmillan UK (PUBLISHER) |
| DOI | 10.1057/978-1-137-59569-0_3 |
| OpenAlex | W2557681715 |
| ISBN | 9781137595690 |
| Language | EN |
| Citations received | 2 |
In Macroanalysis (2013), Matthew Jockers provocatively declares that large digitized collections of literary texts have rendered close reading “totally inappropriate as a method of studying literary history.” Hammond, Brooke and Hirst respond by demonstrating the productive interpretive interplay that results when close reading is placed in a “feedback loop” with the insights available at the scale of big data. Using cutting-edge techniques in computational stylistics, including their own six-dimensional approach to quantifying literary style, Hammond, Brooke and Hirst argue that analytic techniques trained on large datasets can prompt new close readings and, in particular, provide new insight into the dialogism or multi-voicedness of three important modernist texts: T. S. Eliot’s The Waste Land, Virginia Woolf’s To the Lighthouse and James Joyce’s “The Dead.”
Art · Big data · Data mining · Linguistics · Computer Science · Digital Humanities and Scholarship · Opinion Dynamics and Social Influence · Philosophy
| Unique citing works | 2 |
|---|---|
| Citations per year | 0,22 |
| Citation span | 2017 - 2024 (8) |
| Citation velocity | recent |
| Highly cited | No |
| Citation types | Neutral: 2 |