Please use this identifier to cite or link to this item:
https://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 |
Series/Report no.: | Proceedings of the IEEE-ACM International Conference on Advances in Social Networks Analysis and Mining |
ISBN: | 9781728110561 |
ISSN: | 2473-9928 2473-991X |
Conference Name: | IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) (7 Dec 2020 - 10 Dec 2020 : Virtual online) |
Editor: | Atzmüller, M. Coscia, M. Missaoui, R. |
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 |
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: | Aurora harvest 8 Mathematical Sciences publications |
Files in This Item:
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hdl_130213.pdf | Submitted version | 1.84 MB | Adobe PDF | View/Open |
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