Abstract
Identifying and characterizing the dynamics of modern TV series subplots is an open problem. One way is to study the underlying social network of interactions between the characters. Standard dynamic network extraction methods rely on temporal integration, either over the whole considered period, or as a sequence of several time-slices. However, they turn out to be inappropriate in the case of TV series, because the scenes shown on-screen alternatively focus on parallel story lines, and do not necessarily respect a traditional chronology. In this article, we introduce Narrative Smoothing, a novel network extraction method taking advantage of the plot properties to solve some of their limitations. We apply our method to a corpus of three popular series, and compare it to both standard approaches. Narrative smoothing leads to more relevant observations when it comes to the characterization of the protagonists and their relationships, confirming its appropriateness to model the intertwined story lines constituting the plots.
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Notes
- 1.
The website http://moviegalaxies.com/ [12] provides a convenient way of interactively visualizing such cumulative character networks for a database of about 700 movies.
- 2.
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Acknowledgements
This work was supported by the French National Research Agency (ANR) GAFES project (ANR-14-CE24-0022) and the Research Federation Agorantic, University of Avignon.
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Bost, X., Labatut, V., Gueye, S., Linarès, G. (2018). Extraction and Analysis of Dynamic Conversational Networks from TV Series. In: Kaya, M., Kawash, J., Khoury, S., Day, MY. (eds) Social Network Based Big Data Analysis and Applications. Lecture Notes in Social Networks. Springer, Cham. https://doi.org/10.1007/978-3-319-78196-9_3
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