Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/130213
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Type: Conference paper
Title: The one comparing narrative social network extraction techniques
Author: Edwards, M.
Tuke, S.
Roughan, M.
Mitchell, L.
Citation: Proceedings of the IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM2020), 2020 / Atzmüller, M., Coscia, M., Missaoui, R. (ed./s), pp.905-913
Publisher: IEEE
Publisher Place: online
Issue Date: 2020
ISBN: 9781728110561
ISSN: 2473-9928
2473-991X
Conference Name: IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) (07 Dec 2020 - 10 Dec 2020 : Virtual online)
Statement of
Responsibility: 
Michelle Edwards, Jonathan Tuke, Matthew Roughan, Lewis Mitchell
Abstract: Analysing narratives through their social networks is an expanding field in quantitative literary studies. Manually extracting a social network from any narrative can be time consuming, so automatic extraction methods of varying complexity have been developed. However, the effect of different extraction methods on the resulting networks is unknown. Here we model and compare three extraction methods for social networks in narratives: manual extraction, co-occurrence automated extraction and automated extraction using machine learning. Although the manual extraction method produces more precise results in the network analysis, it is highly time consuming. The automatic extraction methods yield comparable results for density, centrality measures and edge weights. Our results provide evidence that automatically-extracted social networks are reliable for many analyses. We also describe which aspects of analysis are not reliable with such a social network. Our findings provide a framework to analyse narratives, which help us improve our understanding of how stories are written and evolve, and how people interact with each other. Index Tenns-social networks, narratives, television
Rights: © 2020 IEEE
RMID: 1000040103
DOI: 10.1109/ASONAM49781.2020.9381346
Grant ID: http://purl.org/au-research/grants/arc/CE140100049
Published version: https://doi.org/10.1109/ASONAM49781.2020
Appears in Collections:Mathematical Sciences publications

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