A character network study of two Sci-Fi TV series

Tan, Meifang; Ujum, Ephrance Abu; Ratnavelu, Kuru

doi:10.1063/1.4866954

Cited by 10 publications

(18 citation statements)

References 4 publications

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“…Discrepancies can also appear in the speaker names. In this case, a simple approach consists in using an a priori list of the characters involved in the script [129,242,272], with their associated aliases. This list is generally constituted manually, or by taking advantage of publicly available resources (generally also constituted manually), such as the Wikipedia page of the considered work of fiction.…”

Section: Semi-structured Textmentioning

confidence: 99%

“…With a power law distribution, the average is not a very informative statistic to describe the degree, as it is not a characteristic value. However, authors traditionally like to indicate it [108,137,154], sometimes with the minimal and maximal degree values [8,277], standard deviation [214], variance [238], or median [242]. In [71,115], the average degree and strength are two among several features used to compare networks extracted from distinct works.…”

Section: /75mentioning

confidence: 99%

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Extraction and Analysis of Fictional Character Networks

Labatut

Bost

2019

ACM Comput. Surv.

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A character network is a graph extracted from a narrative, in which vertices represent characters and edges correspond to interactions between them. A number of narrative-related problems can be addressed automatically through the analysis of character networks, such as summarization, classification, or role detection. Character networks are particularly relevant when considering works of fictions (e.g. novels, plays, movies, TV series), as their exploitation allows developing information retrieval and recommendation systems. However, works of fiction possess specific properties making these tasks harder.This survey aims at presenting and organizing the scientific literature related to the extraction of character networks from works of fiction, as well as their analysis. We first describe the extraction process in a generic way, and explain how its constituting steps are implemented in practice, depending on the medium of the narrative, the goal of the network analysis, and other factors. We then review the descriptive tools used to characterize character networks, with a focus on the way they are interpreted in this context. We illustrate the relevance of character networks by also providing a review of applications derived from their analysis. Finally, we identify the limitations of the existing approaches, and the most promising perspectives.

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Section: Semi-structured Textmentioning

confidence: 99%

Section: /75mentioning

confidence: 99%

Extraction and Analysis of Fictional Character Networks

Labatut

Bost

2019

ACM Comput. Surv.

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“…But when applied to multimedia data and movies, the analysis first focused on scene graphs (Yeung et al 1996;Jung et al 2004;EAC et al 2019) for their potential for summarization. Character networks then became a natural focus for story analysis which from literature (Knuth 1993;Waumans et al 2015;Chen et al 2019) expanded to multimedia content (Weng et al 2009;Tan et al 2014;Tran and Jung 2015;Mish 2016;He et al 2018). Particular attention has been paid to dialogue structure (Park et al 2012;Gorinski and Lapata 2018), which leads to an extension of network modeling to multilayer models (Lv et al 2018;Ren et al 2018;Mourchid et al 2018).…”

Section: Related Workmentioning

confidence: 99%

“…They analyze social centrality and community structure of the network based on human-based ground truth. Tan et al (2014) analyze the topology of character networks in TV series based on their scene co-occurrence in scripts. CoCharNet (Tran and Jung 2015) uses manually annotated co-appearance social network on the six Star Wars movies, and propose a centrality analysis.…”

Section: Related Workmentioning

confidence: 99%

“…Network modelling puts into relation different entities, therefore it has naturally become a powerful tool to capture the elements articulation in stories (Rital et al 2005;Park et al 2012;Waumans et al 2015;Tan et al 2014;Mish 2016;Mourchid et al 2018;Viard and Fournier-S'niehotta 2018;Markovič et al 2018). Such network models have been applied to many different types of stories, starting with written stories in books (Waumans et al 2015;Markovič et al 2018), in news events from news papers and TV , in television series (Tan et al 2014), and eventually in the target medium of this paper: movies (Park et al 2012;Mourchid et al 2018). The topology and structure of these networks have been investigated both visually ) and analytically (Waumans et al 2015;Rital et al 2005), and may in turn be used for prediction tasks (Viard and Fournier-S'niehotta 2018).…”

Section: Introductionmentioning

confidence: 99%

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Movienet: a movie multilayer network model using visual and textual semantic cues

et al. 2019

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Discovering content and stories in movies is one of the most important concepts in multimedia content research studies. Network models have proven to be an efficient choice for this purpose. When an audience watches a movie, they usually compare the characters and the relationships between them. For this reason, most of the models developed so far are based on social networks analysis. They focus essentially on the characters at play. By analyzing characters interactions, we can obtain a broad picture of the narration's content. Other works have proposed to exploit semantic elements such as scenes, dialogues, etc.. However, they are always captured from a single facet. Motivated by these limitations, we introduce in this work a multilayer network model to capture the narration of a movie based on its script, its subtitles, and the movie content. After introducing the model and the extraction process from the raw data, we perform a comparative analysis of the whole 6-movie cycle of the Star Wars saga. Results demonstrate the effectiveness of the proposed framework for video content representation and analysis.

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Multilayer Network Model of Movie Script

Mourchid¹,

Renoust²,

Cherifi³

2018

Studies in Computational Intelligence

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Network models have been increasingly used in the past years to support summarization and analysis of narratives, such as famous TV series, books and news. Inspired by social network analysis, most of these models focus on the characters at play. The network model well captures all characters interactions, giving a broad picture of the narration's content. A few works went beyond by introducing additional semantic elements, always captured in a single layer network. In contrast, we introduce in this work a multilayer network model to capture more elements of the narration of a movie from its script: people, locations, and other semantic elements. This model enables new measures and insights on movies. We demonstrate this model on two very popular movies.

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A character network study of two Sci-Fi TV series

Cited by 10 publications

References 4 publications

Extraction and Analysis of Fictional Character Networks

Extraction and Analysis of Fictional Character Networks

Movienet: a movie multilayer network model using visual and textual semantic cues

Multilayer Network Model of Movie Script

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