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Computational Models of Readerly Affect

Our experience of suspense is central to our enjoyment of narrative media. Whether reading books or watching films, the particular mixture of anticipation and concern that characterizes suspense works to maintain the our interest even while it propels us through the narrative. But why do some readers feel suspense in books, or while watching films, that leave other audiences wanting more? What features underlie our experience of suspense? And most importantly, if suspense is linked to our desire to know what happens next, why do we still feel it when re-reading a book, or re-watching a movie, when we know the outcome? In this project, we use a series of computational models to first detect passages that seem to produce suspenseful reactions in the majority of readers, and then investigate the features that these passages share. By modeling an affective response, like suspense, using features from the text, we are not only able to understand how suspensefulness has changed over time, but also what parts of a book create the conditions for the possibility of the experience of the affect in the first place.

Speaker: Mark Algee-Hewitt, Stanford

Monday, 04/08/19


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Stanford Symbolic Systems Forum

Margaret Jacks Hall
Stanford, CA 94305

Website: Click to Visit