Cultural Analytics Series

An in-person, collaborative deep dive with Cultural Analytics speaker Kyle Booten.

Large language models can make writing mind-numbingly efficient — but the point of writing with AI should be to write what we couldn’t have written alone (without generating bland, derivative “slop”).

Miguel Escobar Varela studies changes in Southeast Asian cultural heritage, combining fieldwork with computational methods.

Miguel Escobar Varela studies changes in Southeast Asian cultural heritage, combining fieldwork with computational methods.

David Bamman’s research focuses on natural language processing and cultural analytics, applying NLP and AI to empirical questions in the humanities and social sciences. 

Naitian Zhou is a Ph.D. stu­dent whose re­search cen­ters on de­vel­op­ing com­pu­ta­tional meth­ods to un­der­stand mean­ing em­bed­ded in style.

Peter Forberg is a Ph.D. student in sociology who studies technology, political movements, governance, and ethnography. 

Noah Askin is a computational social scientist and sociologist who studies the creative process, its outcomes, and the forces that influence it.

Noah Askin is a computational social scientist and sociologist who studies the creative process, its outcomes, and the forces that influence it.

Ongoing work on benchmarking vision-language models and using them and object detection for art historical research into canonicity and national romanticism styles in Northern Europe.

Ongoing work on how satire is communicated in both text and images in the satirical journal Corsaren using image and text embeddings.

Svenja Guhr’s research uses computational methods to model suspense in American and British short fiction.

Taylor Arnold uses large-scale computational methods to analyze how television production practices and narrative strategies intersect with industry changes and cultural contexts.

Lauren Tilton’s research applies digital and computational methods to the study of 20th and 21st century documentary expression and visual culture.

While multimodal large language models (LLMs) excel at dialogue, whether they can adequately parse the structure of conversation — conversational roles and threading — remains underexplored.

Peter Leonard itemizes three barriers that hinder analysis of film and television.