Extraction and Analysis of Dynamic Conversational Networks from TV Series

TL;DR

This paper introduces Narrative Smoothing, a new method for extracting dynamic social networks from TV series that better captures parallel storylines and character interactions, improving analysis accuracy.

Contribution

The paper presents Narrative Smoothing, a novel network extraction technique tailored for TV series, addressing limitations of traditional methods in handling non-linear storylines.

Findings

Narrative Smoothing provides more relevant character relationship insights.

It outperforms standard approaches in modeling intertwined storylines.

The method enhances understanding of protagonist dynamics.

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 onscreen alternatively focus on parallel storylines, 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 3 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 storylines constituting the plots.