Literature review on transmedia and narrative using an unsupervised machine learning algorithm
DOI:
https://doi.org/10.26441/RC24.2-2025-3942Keywords:
Transmedia, narrative, communication strategy, literature review, ScopusAbstract
Purpose. The study's main objective is to analyze publications on transmedia and narrative in the Scopus database, identifying the evolution of terms by year, to propose a classification according to the broad themes found. Methodology. We applied an unsupervised machine learning algorithm, called topic modeling, to identify the main themes addressed. Then, we apply open and axial coding from grounded theory to identify important concepts, aggregate them, and propose higher-order themes that can be used to classify the knowledge generated on transmedia and narrative. Results and conclusions. Five research themes are revealed, displayed in an evolutionary model: Convergence between platforms: games, television, and movies; Intermediality, authorship, and semiotic universes; Semiotic analysis and multimodality in transmedia narratives; Political and cultural interpretations in transmedia journalism; and Exploring transmedia narrative in the digital environment. Furthermore, multimodal storytelling stands out as a crucial field of research for understanding the new ways in which users interact with transmedia content across various platforms and formats. This paper highlights the need for new professional profiles and new communication strategies to understand the effects of transmedia storytelling, harnessing the potential of multimodality in the digital age, without neglecting the story to be told and the impact on the user through appropriate monitoring. Original contribution. It combines quantitative and qualitative methods to identify five major themes and their evolution over time, proposing a solid conceptual basis for future research and professional applications.
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