Literature DB >> 33788879

The Simpsons did it: Exploring the film trope space and its large scale structure.

Pablo García-Sánchez1, Antonio Velez-Estevez2, Juan Julián Merelo1, Manuel Jesús Cobo2.   

Abstract

Creating a story is a challenging task due to the the complex relations between the parts that make it up, which is why many new stories are built on those cohesive elements or patterns, called tropes that have been shown to work in the past. A trope is a recurring storytelling device or pattern, or sometimes a meta-element, used by the authors to express ideas that the audience can recognize or relate to, such as the Hero's Journey. Discovering tropes and how they cluster in popular works and doing it at scale to generate new plots may benefit writers; in this paper, we analyze them and use a principled procedure to identify trope combinations, or communities, that could possible be successful. The degree of development of these different communities can help us identify areas that are under-developed and, thus, susceptible to such a type of development. To detect these communities, with their associated degree of development and interest, we propose a methodology based on scientometric and complex network analysis techniques. As a secondary objective, we will obtain a general perspective in the trope and films network: the tropesphere. We have used a dataset of 10,766 movies and 25,776 tropes associated with them, together with rating, genres and popularity. Our analysis has shown that not only there are different trope communities associated with specific genres, and that there are significant differences between the rating and popularity of these communities but also there are differences on the level of development between them: emerging/declining, specific, transversal or motor.

Entities:  

Year:  2021        PMID: 33788879      PMCID: PMC8011780          DOI: 10.1371/journal.pone.0248881

Source DB:  PubMed          Journal:  PLoS One        ISSN: 1932-6203            Impact factor:   3.240


  4 in total

1.  Fast algorithm for detecting community structure in networks.

Authors:  M E J Newman
Journal:  Phys Rev E Stat Nonlin Soft Matter Phys       Date:  2004-06-18

Review 2.  Science of science.

Authors:  Santo Fortunato; Carl T Bergstrom; Katy Börner; James A Evans; Dirk Helbing; Staša Milojević; Alexander M Petersen; Filippo Radicchi; Roberta Sinatra; Brian Uzzi; Alessandro Vespignani; Ludo Waltman; Dashun Wang; Albert-László Barabási
Journal:  Science       Date:  2018-03-02       Impact factor: 47.728

3.  Topology analysis of social networks extracted from literature.

Authors:  Michaël C Waumans; Thibaut Nicodème; Hugues Bersini
Journal:  PLoS One       Date:  2015-06-03       Impact factor: 3.240

4.  From Louvain to Leiden: guaranteeing well-connected communities.

Authors:  V A Traag; L Waltman; N J van Eck
Journal:  Sci Rep       Date:  2019-03-26       Impact factor: 4.379

  4 in total

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