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Modern popular TV series often develop complex storylines spanning severalseasons, but are usually watched in quite a discontinuous way. As a result, theviewer generally needs a comprehensive summary of the previous season plotbefore the new one starts. The generation of such summaries requires first toidentify and characterize the dynamics of the series subplots. One way of doingso is to study the underlying social network of interactions between thecharacters involved in the narrative. The standard tools used in the SocialNetworks Analysis field to extract such a network rely on an integration oftime, either over the whole considered period, or as a sequence of severaltime-slices. However, they turn out to be inappropriate in the case of TVseries, due to the fact the scenes showed onscreen alternatively focus onparallel storylines, and do not necessarily respect a traditional chronology.This makes existing extraction methods inefficient to describe the dynamics ofrelationships between characters, or to get a relevant instantaneous view ofthe current social state in the plot. This is especially true for charactersshown as interacting with each other at some previous point in the plot buttemporarily neglected by the narrative. In this article, we introduce narrativesmoothing, a novel, still exploratory, network extraction method. It smoothsthe relationship dynamics based on the plot properties, aiming at solving someof the limitations present in the standard approaches. In order to assess ourmethod, we apply it to a new corpus of 3 popular TV series, and compare it toboth standard approaches. Our results are promising, showing narrativesmoothing leads to more relevant observations when it comes to thecharacterization of the protagonists and their relationships. It could be usedas a basis for further modeling the intertwined storylines constituting TVseries plots.

Narrative Smoothing: Dynamic Conversational Network for the Analysis of TV Series Plots