IDEAS home Printed from https://ideas.repec.org/a/pal/palcom/v9y2022i1d10.1057_s41599-022-01352-9.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1057/s41599-022-01352-9
    File Function: Abstract
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1057/s41599-022-01352-9?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. 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.
    2. 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).
    3. 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).
    4. Hennig, Christian, 2007. "Cluster-wise assessment of cluster stability," Computational Statistics & Data Analysis, Elsevier, vol. 52(1), pages 258-271, September.
    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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Ana Alina Tudoran, 2022. "A machine learning approach to identifying decision-making styles for managing customer relationships," Electronic Markets, Springer;IIM University of St. Gallen, vol. 32(1), pages 351-374, March.
    2. Wu, Han-Ming, 2011. "On biological validity indices for soft clustering algorithms for gene expression data," Computational Statistics & Data Analysis, Elsevier, vol. 55(5), pages 1969-1979, May.
    3. Sara Dolnicar & Friedrich Leisch, 2017. "Using segment level stability to select target segments in data-driven market segmentation studies," Marketing Letters, Springer, vol. 28(3), pages 423-436, September.
    4. Nikolaos Karapetsas & Thomas K. Alexandridis & George Bilas & Serafeim Theocharis & Stefanos Koundouras, 2023. "Delineating Natural Terroir Units in Wine Regions Using Geoinformatics," Agriculture, MDPI, vol. 13(3), pages 1-18, March.
    5. Anastasia Panori, 2017. "A Tale of Hidden Cities," REGION, European Regional Science Association, vol. 4, pages 19-38.
    6. Aicha Ait Sair & Kamal Kansou & Franck Michaud & Bernard Cathala, 2021. "Multicriteria Definition of Small-Scale Biorefineries Based on a Statistical Classification," Sustainability, MDPI, vol. 13(13), pages 1-18, June.
    7. Hsu, David, 2015. "Comparison of integrated clustering methods for accurate and stable prediction of building energy consumption data," Applied Energy, Elsevier, vol. 160(C), pages 153-163.
    8. Wolfgang Karl Härdle & Lukas Borke, 2017. "GitHub API based QuantNet Mining infrastructure in R," SFB 649 Discussion Papers SFB649DP2017-008, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    9. Daly, Bonita A. & Schuler, Drue K., 1998. "Redefining a certified public accounting firm," Accounting, Organizations and Society, Elsevier, vol. 23(5-6), pages 549-567.
    10. Patrick Zschech & Kai Heinrich & Raphael Bink & Janis S. Neufeld, 2019. "Prognostic Model Development with Missing Labels," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 61(3), pages 327-343, June.
    11. Sirieix, Lucie & Lála, Jan & Kocmanová, Klára, 2017. "Understanding the antecedents of consumers' attitudes towards doggy bags in restaurants: Concern about food waste, culture, norms and emotions," Journal of Retailing and Consumer Services, Elsevier, vol. 34(C), pages 153-158.
    12. Chiara Mauri & Angelo Di Gregorio & Alice Mazzucchelli & Isabella Maggioni, 2017. "The employability of marketing graduates in the era of digitalisation and globalisation," MERCATI & COMPETITIVIT?, FrancoAngeli Editore, vol. 2017(4), pages 103-124.
    13. Smith, Andrew N. & Fischer, Eileen & Yongjian, Chen, 2012. "How Does Brand-related User-generated Content Differ across YouTube, Facebook, and Twitter?," Journal of Interactive Marketing, Elsevier, vol. 26(2), pages 102-113.
    14. Batool, Fatima & Hennig, Christian, 2021. "Clustering with the Average Silhouette Width," Computational Statistics & Data Analysis, Elsevier, vol. 158(C).
    15. Bolívar, Fernando & Duran, Miguel A. & Lozano-Vivas, Ana, 2023. "Bank business models, size, and profitability," Finance Research Letters, Elsevier, vol. 53(C).
    16. Taran Loper & Victoria L. Crittenden, 2017. "Energy Security: Shaping The Consumer Decision Making Process In Emerging Economies," Organizations and Markets in Emerging Economies, Faculty of Economics, Vilnius University, vol. 8(1).
    17. Sebastian Molinillo & Francisco Rejón-Guardia & Rafael Anaya-Sánchez, 2023. "Exploring the antecedents of customers’ willingness to use service robots in restaurants," Service Business, Springer;Pan-Pacific Business Association, vol. 17(1), pages 167-193, March.
    18. Reder, Maik & Yürüşen, Nurseda Y. & Melero, Julio J., 2018. "Data-driven learning framework for associating weather conditions and wind turbine failures," Reliability Engineering and System Safety, Elsevier, vol. 169(C), pages 554-569.
    19. Marcin Gąsior, 2021. "Environmental Attitudes and Willingness to Purchase Online—Classification Approach," Sustainability, MDPI, vol. 13(15), pages 1-17, August.
    20. Aurélie Ringeval-Deluze & Louis César Ndione, 2021. "Meaning of packaging and innovation dynamics in a traditional luxury sector: the example of growers’ champagne [Signification des packagings et dynamiques d’innovation dans un secteur de luxe tradi," Post-Print hal-03266127, HAL.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:pal:palcom:v:9:y:2022:i:1:d:10.1057_s41599-022-01352-9. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: https://www.nature.com/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.