Windows on Waverley: exploring the effect of variations in the construction of literary social networks

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Title: Windows on Waverley: exploring the effect of variations in the construction of literary social networks
Authors: Wade, KarenGrayson, Siobhán
Permanent link: http://hdl.handle.net/10197/9020
Date: 10-Sep-2016
Online since: 2017-10-27T12:12:23Z
Abstract: 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.
Funding Details: Irish Research Council
Science Foundation Ireland
Funding Details: Insight Research Centre
Type of material: Conference Publication
Keywords: Machine Learning & Statistics
Other versions: https://hridigital.shef.ac.uk/dhc/2016/paper/74
Language: en
Status of Item: Peer reviewed
Conference Details: Digital Humanities Congress. University of Sheffield, United Kingdom
This item is made available under a Creative Commons License: https://creativecommons.org/licenses/by-nc-nd/3.0/ie/
Appears in Collections:Insight Research Collection

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