Literature, Data, and AI

What kind of data is literature? What different methods are available to scholars who work with it, and what are the philosophical assumptions that underpin those methods? In this course, we will survey major critical approaches to literature from the last century as well as emerging methods from the digital humanities, and try them out for ourselves. Students will construct their own portfolio of texts and each week they will (re)analyze them using a different approach; they will record their findings and reflect on their experiences in a weekly log. The course will comprise asynchronous activities (lectures, presentations, assignments, readings) and one synchronous meeting per week to discuss the readings. Approaches may include: formalism, structuralism, Marxism, psychoanalysis, critical approaches to identity and performance (gender, race, sexuality and disability), network analysis, topic modeling, stylometry, and word embeddings. No prior programming knowledge is expected. This course will not offer detailed training in computational analysis; rather, the focus will be on the theoretical implications of computational tools. All readings will be in English.