Speaker: Andrew Piper and Sunyam Bagga
Affiliation: McGill University, 680 Sherbrooke St., Montreal, QC H3A 2M7, Canada
Title: A Quantitative Study of Fictional Things
Abstract: 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. A large body of scholarship known as ``thing theory’’ has attempted to understand the function of fictional things within literature mostly by focusing on small case studies. 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.
