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Type: Thesis
Title: The One with the Social Network Analysis: the extraction, analysis and modelling of temporal social networks from narratives
Author: Edwards, Michelle Claire
Issue Date: 2019
School/Discipline: School of Mathematical Sciences
Abstract: Narratives tell us about the people, cultures, and time periods in and about which they were written. Therefore, narrative analysis is a powerful tool for understanding culture. One way to analyse narratives is through their social networks, however extracting the network is a complex task. Manually recording characters and their interactions is an accurate, but time consuming method for narrative social network extraction, however efficient automatic extraction methods may introduce errors. In this thesis, we perform a detailed comparative study of narrative social network extraction techniques, and investigate the effect the techniques have on the analysis of the narrative. We use the 1994–2004 television series Friends as a case study to model and compare extraction techniques. By designing a simulated social network and observation processes resembling different network extraction techniques, we find that automated network extraction methods are reliable for computing many network metrics, but can distort the clustering coefficient. Our comparison of extraction techniques allows for many more narratives to be extracted and analysed efficiently. We also analyse and model the social networks of Friends, to gain new insights into the the series, and what made it successful. We show which are the most important characters and relationships, and through modelling social network features we find the most informative features to predict success. Our analysis of Friends provides an example and a building block for deeper understanding about particular narratives and narratives in general.
Advisor: Roughan, Matthew
Mitchell, Lewis
Tuke, Jono
Dissertation Note: Thesis (MPhil) -- University of Adelaide, School of Mathematical Sciences, 2019
Keywords: Social networks
narrative analysis
network extraction
Provenance: This electronic version is made publicly available by the University of Adelaide in accordance with its open access policy for student theses. Copyright in this thesis remains with the author. This thesis may incorporate third party material which has been used by the author pursuant to Fair Dealing exceptions. If you are the owner of any included third party copyright material you wish to be removed from this electronic version, please complete the take down form located at:
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