We have a new paper out on the quantitative study of fictional things appearing as part of the Computational Humanities Research workshop. If you aren’t familiar with CHR I strongly recommend you check it out. It is a great venue for computational humanities research.
Over the past two decades, a large body of research has emerged in the field of literary studies focusing on the question of “things.” “Thing theory,” as this area has come to be known, has its origins in the rise of material cultural studies, new historicism, and media theory. In this paper, we apply machine learning based predictive models on two large data sets of historical and contemporary fiction to better understand the role that things play in fictional writing. Despite a wealth of recent case studies that focus on particular types of things in individual books, only one work to date has used computational methods to study the broader distribution of fictional objects. If we want to understand what Bill Brown has called “a genuine sense of the things that comprise the stage on which human action, including the action of thought, unfolds,” then it is imperative that we develop methods that can more sufficiently account for the broader population of things in creative writing.
We provide the first-ever estimates of the distribution of different types of things in English-language fiction over the past two centuries along with experiments to model their semantic identity. Our findings suggest that the most common fictional things are structural in nature, functioning akin to narrative props. We conclude by showing how these findings pose problems for inherited theories of fictional things and propose an alternative theoretical framework, embodied cognition, as a way of understanding the predominance of structural things.