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Wade, Karen
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Wade, Karen
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Wade, Karen
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Now showing 1 - 4 of 4
- PublicationWindows on Waverley: exploring the effect of variations in the construction of literary social networks(2016-09-10)
; In recent years, social network analysis (SNA) has become increasingly popular as a quantitative approach to the examination of literary works, allowing researchers to generate abstract models of character groupings and interactions that appear in texts, and providing new opportunities for the evaluation of theories about communities and societies in literature. The social networks that are generated for a given novel, however, will differ considerably depending on what choices are made in relation to their construction: what types of interactions or co-occurrences are examined, what characters or other entities are considered, whether full texts or subsections such as chapters are investigated, and what automated methods are utilised for extracting character data, among others. This paper examines the effect of varying one specific aspect of network construction, by applying different "sliding window" strategies in order to create variations on social networks in three rather different early 19th-century novels: Pride and Prejudice (1813), Waverley (1814), and Frankenstein (1818). Three window strategies (collinear, co-planar and combination) are discussed, each of which captures qualitatively different social links between characters. We argue that the resulting networks yield different insights into a variety of aspects of the novels' construction, including narrative style and interactions between characters of different social class. We also suggest that rather than seeking to determine a single best-practice methodology for literary SNA, it may instead be illuminating to experiment with different approaches to the modelling of literary texts as social networks.85 - PublicationExploring the Role of Gender in 19th Century Fiction Through the Lens of Word Embeddings(2017-06-20)
; ; ; ; Within the last decade, substantial advances have been made in the field of computational linguistics, due in part to the evolution of word embedding algorithms inspired by neural network models. These algorithms attempt to derive a set of vectors which represent the vocabulary of a textual corpus in a new embedded space. This new representation can then be used to measure the underlying similarity between words. In this paper, we explore the role an author's gender may play in the selection of words that they choose to construct their narratives. Using a curated corpus of forty-eight 19th century novels, we generate, visualise, and investigate word embedding representations using a list of gender-encoded words. This allows us to explore the different ways in which male and female authors of this corpus use terms relating to contemporary understandings of gender and gender roles.1322 - PublicationDiscovering Structure in Social Networks of 19th Century Fiction(ACM, 2016-05-25)
; ; ; ; ; Inspired by the increasing availability of large text corpora online, digital humanities scholars are adopting computational approaches to explore questions in the field of literature from new perspectives. In this paper, we examine detailed social networks of characters, extracted from several works of 19th century fiction by Jane Austen and Charles Dickens. This allows us to apply methodologies from social network analysis, such as community detection, to explore the structure of these networks. By evaluating the results in collaboration with literary scholars, we find that the structure of the character networks can reveal underlying structural aspects within a novel, particularly in relation to plot and characterisation.709Scopus© Citations 10 - PublicationNovel2Vec: Characterising 19th Century Fiction via Word Embeddings(2016-09-21)
; ; ; ; Recently, considerable attention has been paid to word embedding algorithms inspired by neural network models. Given a large textual corpus, these algorithms attempt to derive a set of vectors which represent the corpus vocabulary in a new embedded space. This representation can provide a useful means of measuring the underlying similarity between words. Here we investigate this property in the context of annotated texts of 19th-century fiction by the authors Jane Austen, Charles Dickens, and Arthur Conan Doyle. We demonstrate that building word embeddings on these texts can provide us with an insight into how characters group differently under different conditions, allowing us to make comparisons across different novels and authors. These results suggest that word embeddings can potentially provide a useful tool in supporting quantitative literary analysis.1344