Multimedia Tools and Applications

, Volume 76, Issue 8, pp 10371–10388 | Cite as

A computational model of transmedia ecosystem for story-based contents

  • Jai E. Jung
  • O-Joun Lee
  • Eun-Soon You
  • Myoung-Hee Nam


Story-based contents (e.g., novel, movies, and computer games) have been dynamically transformed into various media. In this environment, the contents are not complete in themselves, but closely connected with each other. Also, they are not simply transformed form a medium to other media, but expanding their stories. It is called as a transmedia storytelling, and a group of contents following it is called as a transmedia ecosystem. Since the contents are highly connected in terms of the story in the transmedia ecosystem, the existing content analysis methods are hard to extract relationships between the contents. Therefore, a proper content analysis method is needed with considering expansions of the story. The aim of this work is to understand how (and why) such contents are transformed by i) defining the main features of the transmedia storytelling and ii) building the taxonomy among the transmedia patterns. More importantly, computational transmedia ecosystem is designed to process a large number of the contents, and to support high understandability of the complex transmedia patterns.


Transmedia Storytelling Multimedia analysis Digital contents Computational ecosystem 



This work was supported by the Ministry of Education of the Republic of Korea and the National Research Foundation of Korea (NRF-2015S1A5B6037297).


  1. 1.
    Aarseth E (2006) The culture and business of cross-media productions. Pop Commun 4(3):203–211CrossRefGoogle Scholar
  2. 2.
    Boutemedjet S, Ziou D (2008) A graphical model for context-aware visual content recommendation. IEEE Trans Multimedia 10(1):52–62CrossRefGoogle Scholar
  3. 3.
    Brooker W (2009) All our variant futures: the many narratives of blade runner: the final cut. Pop Commun 7(2):79–91CrossRefGoogle Scholar
  4. 4.
    Chunwijitra S, Berena AJ, Okada H, Ueno H (2013) Advanced content authoring and viewing tools using aggregated video and slide synchronization by key marking for web-based e-learning system in higher education. IEICE Trans Inf Syst E96-D(8):1754–1765CrossRefGoogle Scholar
  5. 5.
    Du J, Xu H, Huang X (2014) Box office prediction based on microblog. Expert Syst Appl 41(4):1680–1689CrossRefGoogle Scholar
  6. 6.
    Fienberg SE (2010) The prehistory of the center for statistics and the social sciences, with a prequel and epilogue. Stat Methodol 7(3):175–186MathSciNetCrossRefMATHGoogle Scholar
  7. 7.
    Harvey CB (2015) Fantastic Transmedia, chap. of Hobbits and Hulks: Adaptation Versus Narrative Expansion, pp 63–92 Palgrave Macmillan UKGoogle Scholar
  8. 8.
    Jenkins H (2006) Convergence Culture: Where Old and New Media Collide New York University PressGoogle Scholar
  9. 9.
    Jung JJ, You E, Park S (2013) Emotion-based character clustering for managing story-based contents: a cinemetric analysis. Multimedia Tools Appl 65(1):29–45CrossRefGoogle Scholar
  10. 10.
    Krizanovich K (2010) The reboot: Franchise rejuvenation in the film-product life cycle. Ph.D. thesis City University, LondonGoogle Scholar
  11. 11.
    Long GA (2007) Transmedia storytelling: Business, aesthetics and production at the jim henson company. Ph.D. thesis Massachusetts Institute of TechnologyGoogle Scholar
  12. 12.
    Manning S (2005) Managing project networks as dynamic organizational forms: Learning from the tv movie industry. Int J Proj Manag 23(5):410–414CrossRefGoogle Scholar
  13. 13.
    McKee R (1997) Substance, Structure, Style, and the Principles of Screenwriting. HarperCollins, New YorkGoogle Scholar
  14. 14.
    Meixner B, Matusik K, Grill C, Kosch H (2014) Towards an easy to use authoring tool for interactive non-linear video. Multimedia Tools Appl 70(2):1251–1276CrossRefGoogle Scholar
  15. 15.
    Menard D (2015) Entertainment assembled: The marvel cinematic universe, a case study in transmedia. Liverty University, Master’s thesisGoogle Scholar
  16. 16.
    Mestyán M, Yasseri T, Kertész J (2013) Early prediction of movie box office success based on wikipedia activity big data. PLoS ONE 8(8):e71,226CrossRefGoogle Scholar
  17. 17.
    Moon S, Bergey PK, Iacobucci D (2010) Dynamic effects among movie ratings, movie revenues, and viewer satisfaction. J Mark 74(1):108–121CrossRefGoogle Scholar
  18. 18.
    Phillips A (2012) A creators guide to transmedia storytelling:how to captivate and engage audiences across multiple platforms McGraw Hill ProfessionalGoogle Scholar
  19. 19.
    Pratten R (2011) Getting started in transmedia storytelling: A practical guide for beginners CreateSpaceGoogle Scholar
  20. 20.
    Scolari CA (2009) Transmedia storytelling: Implicit consumers, narrative worlds, and branding in contemporary media production. Int J Commun 3:586–606Google Scholar
  21. 21.
    Sharda R, Delen D (2006) Predicting box-office success of motion pictures with neural networks. Expert Syst Appl 30(2):243?254CrossRefGoogle Scholar
  22. 22.
    Shmueli E, Kagian A, Koren Y, Lempel R (2012) Care to comment?: recommendations for commenting on news stories. In: Proceedings of the 21st international conference on World Wide Web, pp 429-438. ACM, ACM New York, Lyon, FranceGoogle Scholar
  23. 23.
    Tryon C (2013) Reboot cinema. Convergence: The International Journal of Research into New Media Technologies, vol 19Google Scholar
  24. 24.
    Xia F, Asabere NY, Ahmed AM, Li J, Kong X (2013) Mobile multimedia recommendation in smart communities: A survey. IEEE Access 1:606–624CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  • Jai E. Jung
    • 1
  • O-Joun Lee
    • 1
  • Eun-Soon You
    • 2
  • Myoung-Hee Nam
    • 3
  1. 1.Department of Computer Science and EngineeringChung-Ang UniversitySeoulKorea
  2. 2.Department of French CivilizationInha UniversityIncheonKorea
  3. 3.Department of Theater and Film StudiesInha UniversityIncheonKorea

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