Extraction and Analysis of Fictional Character Networks

Labatut, Vincent; Bost, Xavier

doi:10.1145/3344548

Cited by 85 publications

(69 citation statements)

References 167 publications

(629 reference statements)

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“…Various studies have been conducted for extracting social networks in narrative artworks and discovering narrative features from the social networks [46]. These studies extracted the social networks from texts [47,48], videos [33,49,50], or both of them [51,52].…”

Section: Related Workmentioning

confidence: 99%

Discovering Social Desires and Conflicts from Subculture Narrative Multimedia

et al. 2020

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This study aims at discovering social desires and conflicts from subculture narrative multimedia. Since one of the primary purposes in the subculture consumption is vicarious satisfaction, the subculture works straightforwardly describe what their readers want to achieve and break down. The latent desires and conflicts are useful for understanding our society and realizing smart governance. To discover the social issues, we concentrate on that each subculture genre has a unique imaginary world that consists of inventive subjects. We suppose that the subjects correspond to individual social issues. For example, game fiction, one of the popular genres, describes a world like video games. Under game systems, everyone gets the same results for the same efforts, and it can be interpreted as critics for the social inequality issue. Therefore, we first extract subjects of genres and measure the membership degrees of subculture works for each genre. Using the subjects and membership degrees, we build a genealogy tree of subculture genres by tracing their evolution and differentiation. Then, we extract social issues by searching for the subjects that come from the real world, not imaginary. If a subculture work criticizes authoritarianism, it might include subjects such as government officials and bureaucrats. A combination of the social issues and genre genealogy tree will show diachronic changes in our society. We have evaluated the proposed methods by extracting social issues reflected in Korean web novels.

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Section: Related Workmentioning

confidence: 99%

Discovering Social Desires and Conflicts from Subculture Narrative Multimedia

et al. 2020

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“…Various studies [1][2][3][4] have been conducted for character networks (i.e., social networks between characters that appear in stories) to analyze stories in narrative multimedia (i.e., creative works that contain stories and are distributed through multimedia) automatically. They applied the analysis results on various applications-summarizing [5,6], recommending [7,8], indexing [9,10], and even generating [11] narrative multimedia.…”

Section: Introductionmentioning

confidence: 99%

Learning Hierarchical Representations of Stories by Using Multi-Layered Structures in Narrative Multimedia

Lee

Jung

Kim

2020

Sensors

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Narrative works (e.g., novels and movies) consist of various utterances (e.g., scenes and episodes) with multi-layered structures. However, the existing studies aimed to embed only stories in a narrative work. By covering other granularity levels, we can easily compare narrative utterances that are coarser (e.g., movie series) or finer (e.g., scenes) than a narrative work. We apply the multi-layered structures on learning hierarchical representations of the narrative utterances. To represent coarser utterances, we consider adjacency and appearance of finer utterances in the coarser ones. For the movies, we suppose a four-layered structure (character roles ∈ characters ∈ scenes ∈ movies) and propose three learning methods bridging the layers: Char2Vec, Scene2Vec, and Hierarchical Story2Vec. Char2Vec represents a character by using dynamic changes in the character’s roles. To find the character roles, we use substructures of character networks (i.e., dynamic social networks of characters). A scene describes an event. Interactions between characters in the scene are designed to describe the event. Scene2Vec learns representations of a scene from interactions between characters in the scene. A story is a series of events. Meanings of the story are affected by order of the events as well as their content. Hierarchical Story2Vec uses sequential order of scenes to represent stories. The proposed model has been evaluated by estimating the similarity between narrative utterances in real movies.

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“…The use of network theory is receiving increasing attention in the quest for shedding light on questions related to the social network underlying fictional narratives in general. According to [1], "a number of narrative-related problems can be addressed automatically through the analysis of character networks". They explicitly mention summarization, classification, and role detection.…”

Section: Introductionmentioning

confidence: 99%

“…In fact, there Similar Yet Different has been a lot of discussion regarding similarities between, e.g., Seinfeld and Friends, or Friends and How I Met Your Mother. Such discussions take place mostly in Internet forums and are mainly driven by passionate views of fans of this or that show, some of them completely rejecting views of others 1 .…”

Section: Introductionmentioning

confidence: 99%

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Similar Yet Different: the Structure of Social Networks of Characters in Seinfeld, Friends, How I met Your Mother, and The Big Bang Theory

Bazzan

2020

RITA

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Networktheoryhasbeenusedtoanalyzestructuresofnarrativesinworksoffiction.Indeed,previous works have shed light on issues related to role detection, for instance. However, few comparative works exist that deal with TV shows. Since these shows are very popular, there are several Internet forums that suggest how similar some of them are, mostly by comparing roles or importance of core characters. Is this popular intuition backed by an objective, numerical analysis using tools from network theory? The goal of this paper is to compare four situation comedies (Seinfeld, Friends, How I Met Your Mother, and The Big Bang Theory) that share a lot in common since their characters are friends living in similar, urban, environments, struggling with their daily lives, careers, and so on. Using tools for analyzing social networks, these shows were compared, showing that their structures and the roles of the core characters are fundamentally different. The only measure that proved to be similar among the four shows is entropy of their graphs, especially when computed over the degree distribution.

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Extraction and Analysis of Fictional Character Networks

Cited by 85 publications

References 167 publications

Discovering Social Desires and Conflicts from Subculture Narrative Multimedia

Discovering Social Desires and Conflicts from Subculture Narrative Multimedia

Learning Hierarchical Representations of Stories by Using Multi-Layered Structures in Narrative Multimedia

Similar Yet Different: the Structure of Social Networks of Characters in Seinfeld, Friends, How I met Your Mother, and The Big Bang Theory

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