Computational Humanities


4 units

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Course Description

Critical Approaches to Literature as Data

What can computation allow us to discover about literature and how might machine learning transform literary theory? Can we use algorithms to challenge gender norms and complicate ideas about race and authorship in novels and other narratives? And what can quantification tell us about the markers of poetic form, stylistic innovation, or literary prestige? This course will study these and other issues in the field of digital humanities or cultural analytics to teach humanities scholars computational methods to develop new insights in their disciplines and instruct students of data science in literary and cultural theories to deepen and nuance their approach to data. Open to both undergraduate and graduate students, our course will pursue three ends: first, we will explore the landscape of methods currently used by scholars at the intersection of computation and cultural theory (including methods in natural language processing such as named entity recognition, coreference resolution and parsing; computer vision, including image recognition; classification; social network analysis; and hypothesis testing), and discuss others that are at the leading edge of adoption. Second, we will cast a critical eye on those methods and discuss the assumptions they make about their objects of study in order to determine when they can be appropriately applied. And third, we will lead students through the act of operationalizing a research question in the digital humanities. The course will be structured in two parts: the first will focus on reading articles in the field and detailing the technical knowledge needed to execute that work. We will focus on research that touches several core themes — theories of gender, race, and class, narratology (including free indirect discourse and focalization, or representations of consciousness), poetic theory and meter, and structuralist philosophy — and explore several modalities, including text, images and sound. The second part of the course will be structured as a lab/studio/critique, where students lead discussion about their specific research projects and receive feedback from the class and professors. Our hope is that you will leave the class with the knowledge to carry forward your own projects in computation and critique.


Data 8 (or equivalent); proficient programming in Python
Last updated: June 10, 2022