Abstract

Narration is a universal human practice that serves as a key site of education, collective memory, fostering social belief systems, and furthering human creativity. Recent studies in economics (Shiller, 2020), climate science (Bushell et al., 2017), political polarization (Kubin et al., 2021), and mental health (Adler et al., 2016) suggest an emerging interdisciplinary consensus that narrative is a central concept for understanding human behavior and beliefs. For close to half a century, the field of narratology has developed a rich set of theoretical frameworks for understanding narrative. And yet these theories have largely gone untested on large, heterogenous collections of texts. Scholars continue to generate schemas by extrapolating from small numbers of manually observed documents. In this talk, I will discuss how we can use machine learning to develop data-driven theories of narration to better understand what Labov and Waletzky called “the simplest and most fundamental narrative structures.” How can machine learning help us approach what we might call a minimal theory of narrativity?

Biography

Andrew Piper is Professor and William Dawson Scholar in the Department of Languages, Literatures, and Cultures at McGill University. He is the director of _.txtlab

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a laboratory for cultural analytics, and editor of the /Journal of Cultural Analytics/, an open-access journal dedicated to the computational study of culture. He is the author of numerous books and articles on the relationship of technology and reading, including /Book Was There: Reading in Electronic Times/(Chicago 2012), /Enumerations: Data and Literary Study/(Chicago 2018), and most recently, /Can We Be Wrong? The Problem of Textual Evidence in a Time of Data/(Cambridge 2020).