Revealing structures in transmedia storytelling for the purposes of analysis and classification.

Abstract

Transmedia storytelling involves telling a story using multiple distinct media. The remit of stories that fall under this broad definition is vast, resulting in many words used to describe different categories of transmedia storytelling. There are two problems that arise from this. Firstly, there is a lack of critical tools that are able to be applied to all of the different manifestations of transmedia storytelling, disabling us from comparing different experiences using common language. Secondly, the ad‐hoc categories used such as franchises, Alternate Reality Games and escape rooms are often too broad and not useful to contextualise transmedia research. The ability to use a tool to differentiate between different categories, group experiences together and apply relevant theories within these groups is a challenge that has not yet been accomplished. In this thesis, I have presented a model for describing structural features of transmedia stories (MOTS). I have analysed eight transmedia stories using this model, producing visualisations that show the high‐level structure of each. I have also explored how this model can be used to extract metrics from experiences that can be used as the basis for classification, and have extracted the metrics of fifty experiences. I use a statistical clustering technique known as K‐Means to process these metrics to reveal fourteen distinct categories. I explicate these categories and demonstrate how this classification is useful by referring to literature in the field by applying and updating theories in light of the categories found. The process has made clear that no model or classification is correct or empirical, but instead unique in their ability to produce results that are useful depending on the features of transmedia storytelling that are focused on.

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ORCID for David Millard: orcid.org/0000-0002-7512-2710

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