Proposal for analyzing player emotions in an interactive narrative using story intention graphs

Many contemporary interactive digital narratives consist of cinematic content that combines performance, sound and dialogue with conventional patterns of player interactions that dictate the story path. The non-linear nature of the content makes applying methods of annotation and analysis currently used in text and film challenging, as any particular experience may differ in significant ways and a player's response may occur at different times in the experience while still applying to a particular event. As a result, most studies approach analyzing player experience using either qualitative methods or quantitative methods alone, and often these studies focus on experimental systems. We developed a coding scheme that captures opportunities and decisions and provides hooks for annotating story content and player affective response. We plan to annotate recorded player traversals of The Wolf Among Us, a choice-based adventure game, incorporating a non-verbal report method, the Sensual Evaluation Instrument [6], guided by a text-based intermediate format that models the game logic. We use David Elson's Story Intention Graph to annotate the story content [2]. The proposed study will be evaluated by whether the responses capture salient story content between different traversals effectively. We describe a proposed baseline method of manually identifying shared story segments and tagging emotional content of scenes.