Specific topics, hours, and credit may vary from section to section, year to year.

Course website: https://ca.kentkc.org/ While often defined as “the computational study of culture”, cultural analytics might be best understood as a radical interdisciplinary experiment, one that seeks to understand cultures—socialties, histories, cognition, the literary—through empirical models and patterns, built on effective computational representations of relevant cultural constructs. This experiment calls for a unique skillset: one needs to be familiar with approaches in the interpretive humanities and computer/information science; one also needs to cultivate an interdisciplinary mindset: recognize and appreciate the affordances and limitations of both qualitative and quantitative traditions. This class is imagined as a possible point of departure for those who are so inclined. As such, this course is all about making connections: bridging the interpretive traditions of literary and cultural studies—which guide our critical engagement with texts and cultural artifacts—with computational methods, ranging from featurized classifiers to large language models. It pursues two complementary ends: students will develop interpretive strategies and critical vocabularies for cultural analysis, and, through hands-on practice grounded in engagement and experience with the text, they will learn to represent cultural data—whether text, image, audio, or video—and train machine learning models as algorithmic measuring devices to systematically characterize cultural phenomena of interest. This class welcomes a range of inclinations: maybe you know how to implement an RNN or Transformer from scratch but are curious whether those models can be used to study literature, culture, or something beyond positive or negative sentiments. Or, maybe you’ve experimented with off-the-shelf topic models or word embeddings to explore humanities questions and want to see how far you can take them.

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