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Modeling narrative features in TV series: coding and clustering analysis

Author

Listed:
  • Marta Rocchi

    (Alma Mater Studiorum–University of Bologna)

  • Guglielmo Pescatore

    (Alma Mater Studiorum–University of Bologna)

Abstract

TV series have gained both economic and cultural relevance. Their development over time can hardly be traced back to the simple programmatic action of creative intentionality. Instead, TV series might be studied as narrative ecosystems with emergent trends and patterns. This paper aims to boost quantitative research in the field of media studies, first considering a comparative and data-driven study of the narrative features in the US medical TV series, one of the most popular and longest-running genres on global television. Based on a corpus of more than 400 h of video, we investigate the storytelling evolution of eight audiovisual serial products by identifying three main narrative features (i.e., isotopies). The implemented schematization allows to grasp the basic components of the social interactions showing the strength of the medical genre and its ability to rebuild, in its microcosm, the essential traits of the human macrocosm where random everyday life elements (seen in the medical cases plot) mix and overlap with working and social relationships (professional plot) and personal relationships (sentimental plot). This study relies on data-driven research that combines content analysis and clustering analysis. It significantly differs from traditional studies regarding the narrative features of medical dramas and broadly the field of television studies. We proved that the three isotopies are good descriptors for the medical drama genre and identified four narrative profiles which emphasize the strong stability of these serial products. Contrary to what is often taken for granted in many interpretative studies, creative decisions rarely significantly change the general narrative aspects of the wider series.

Suggested Citation

  • Marta Rocchi & Guglielmo Pescatore, 2022. "Modeling narrative features in TV series: coding and clustering analysis," Palgrave Communications, Palgrave Macmillan, vol. 9(1), pages 1-11, December.
    • Handle: RePEc:pal:palcom:v:9:y:2022:i:1:d:10.1057_s41599-022-01352-9
    DOI: 10.1057/s41599-022-01352-9
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    References listed on IDEAS

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    1. Brock, Guy & Pihur, Vasyl & Datta, Susmita & Datta, Somnath, 2008. "clValid: An R Package for Cluster Validation," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 25(i04).
    2. Hennig, Christian, 2007. "Cluster-wise assessment of cluster stability," Computational Statistics & Data Analysis, Elsevier, vol. 52(1), pages 258-271, September.
    3. Hennig, Christian, 2008. "Dissolution point and isolation robustness: Robustness criteria for general cluster analysis methods," Journal of Multivariate Analysis, Elsevier, vol. 99(6), pages 1154-1176, July.
    4. Charrad, Malika & Ghazzali, Nadia & Boiteau, Véronique & Niknafs, Azam, 2014. "NbClust: An R Package for Determining the Relevant Number of Clusters in a Data Set," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 61(i06).
    5. Kassarjian, Harold H, 1977. "Content Analysis in Consumer Research," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 4(1), pages 8-18, June.
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