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Social Network Analysis of the Professional Community Interaction—Movie Industry Case

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Data Analytics and Management in Data Intensive Domains (DAMDID/RCDL 2021)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1620))

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Abstract

With the rise of the competition in the movie production market, because of new players such as Netflix, Hulu, HBO Max, and Amazon Prime, whose primary goal is producing a large amount of exclusive content in order to gain a competitive advantage, it is extremely important to minimize the number of unsuccessful titles. This paper focuses on new approaches to predict film success, based on the movie industry community structure, and highlights the role of the casting director in movie success. Based on publicly available data we create an “actor”-“casting director”-“talent agent” - “director” communication graph and show that usage of additional knowledge leads to better movie rating prediction.

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Notes

  1. 1.

    https://github.com/karpovilia/cinema.

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Acknowledgements

The article was prepared within the framework of the HSE University Basic Research Program.

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Authors and Affiliations

  1. Higher School of Economics, Moscow, Russia

    Ilia Karpov & Roman Marakulin

Authors
  1. Ilia Karpov
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  2. Roman Marakulin
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Corresponding author

Correspondence to Ilia Karpov .

Editor information

Editors and Affiliations

  1. Space Research Institute of the Russian Academy of Sciences, Moscow, Russia

    Alexei Pozanenko

  2. Federal Research Center “Computer Science and Control” of RAS, Moscow, Russia

    Sergey Stupnikov

  3. Christian-Albrecht University of Kiel, Kiel, Germany

    Bernhard Thalheim

  4. Universidad Carlos III de Madrid, Getafe, Spain

    Eva Mendez

  5. A. A. Baikov Institute of Metallurgy and Materials Science of RAS (IMET RAS), Moscow, Russia

    Nadezhda Kiselyova

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Karpov, I., Marakulin, R. (2022). Social Network Analysis of the Professional Community Interaction—Movie Industry Case. In: Pozanenko, A., Stupnikov, S., Thalheim, B., Mendez, E., Kiselyova, N. (eds) Data Analytics and Management in Data Intensive Domains. DAMDID/RCDL 2021. Communications in Computer and Information Science, vol 1620. Springer, Cham. https://doi.org/10.1007/978-3-031-12285-9_3

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  • DOI: https://doi.org/10.1007/978-3-031-12285-9_3

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-12284-2

  • Online ISBN: 978-3-031-12285-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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