Extraction and Analysis of Dynamic Conversational Networks from TV Series
This work addresses the challenge of modeling social interactions in TV series for researchers in computational social science or media analysis, though it is incremental as it builds on existing network extraction methods.
The authors tackled the problem of analyzing dynamic conversational networks in TV series by introducing Narrative Smoothing, a novel network extraction method that accounts for parallel storylines and non-chronological scenes, leading to more relevant observations about character relationships compared to standard approaches.
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.