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Decoding the Popularity of TV Series: A Network Analysis Perspective

CSEF · 2023 Behavioral & Social Sciences Honorable_mention Award

Yu Melody · 10

Overview

In this project, I explored the possibility of whether network analysis can be used to predict the popularity of TV series. Specifically, I investigated the potential relationship between IMDb reviews and network metrics for three popular TV series; Game of Thrones, House of Cards, and Breaking Bad. In the field of computational linguistics, character networks are used to represent the relationships between characters in a story. These networks are constructed based on the co-occurrence of characters in scenes and can be used to analyze various aspects of the relationships between characters and their impact on the story. To create character networks for each TV series episode, I extracted data on character interactions from a dataset of television scenes. I analyzed multiple network properties, including degree, density, centrality, efficiency, and transitivity. I then performed statistical analyses on the resulting data, which revealed that certain network metrics had a strong correlation with the review scores of the TV series. However, no single metric was consistently identified as having a significant correlation across all three TV series, as the specific network metrics that showed a significant correlation differed. The results of this study could benefit TV producers and writers as they plan and structure episodes of their shows to retain current viewers. While character networks are not the only factor that determines a show’s success, they do play a role in audience enjoyment and should be carefully considered in the production of future seasons.

Source coverage

This record comes from a published award list, not a complete project archive. Its abstract comes from CSEF's public project showcase as archived by the Internet Archive before judging (https://web.archive.org/web/20230401224130/https://ca-csef.zfairs.com/showcase/ShowcaseInfo?f=838e60b7-ea75-46e8-865c-fde4864244b3); the version presented may differ.

Awards (1)

  • Category Award: HM

Competition history

  • CSEF 2023 Behavioral & Social Sciences · Entry S0407

Resources

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