co-founders:

Andrew Salway, Burton Bradstock Research Laboratories
David Herman, Ohio State University
 
Our research explores how computer-assisted analysis of digitized texts provides new resources for the study of narrative. At this stage we are focusing on narrative corpora in the form of written or spoken language, but with recent developments in multimedia computing an analogous approach may be envisioned for other semiotic modes.
 
Our work centers on the following key question: Will coming to terms with large narrative corpora—not single narratives or even groups of stories but rather multimillion-word collections of narratively organized texts—alter the foundational concepts of narrative theory? Thus, rather than trying to develop narratological tools to analyze the narratives now being conveyed or co-enacted in digital environments, we seek to show how understandings of what stories are and how they work may need to be rethought in light of concepts and methods used to study digitized corpora.

To assess how corpus-analytic methods bear on the core concepts and explanatory aims of narrative inquiry, we examine two broad approaches: top-down or hypothesis-driven approaches, and bottom-up or data-driven approaches. Top-down methods have been used in stylistics-based research that begins with categories of structure proposed by prior analysts and then seeks to (dis)confirm their existence—and study their patterns of distribution—in textual corpora. Data-driven methods, for their part, seek to remain as much as possible at the surface level of the texts included in corpora, rather than assuming beforehand that some features will be more relevant than others for the analysis of those texts. The bottom-up approach thus begins with textual features that are computationally tractable, aiming to work up from there to an account of distributional patterns that are distinctively narrative in nature. Such patterns may provide a route of access to to the study of what constitutes narrativity, i.e., the property or set of properties that makes stories interpretable as narratives to begin with. 
 
Our work has implications for research in a range of fields, including narrative analysis, corpus and computational linguistics, social and cognitive psychology, and artificial intelligence research, and can potentially inform the design of systems meant to emulate the experience of being immersed in storyworlds.