  Cultural variation exists between nations (e.g., the United States vs.
China), but also within regions (e.g., California vs. Texas, Los Angeles vs.
San Francisco). Measuring this regional cultural variation can illuminate how
and why people think and behave differently. Historically, it has been
difficult to computationally model cultural variation due to a lack of training
data and scalability constraints. In this work, we introduce a new research
problem for the NLP community: How do we measure variation in cultural
constructs across regions using language? We then provide a scalable solution:
building knowledge-guided lexica to model cultural variation, encouraging
future work at the intersection of NLP and cultural understanding. We also
highlight modern LLMs' failure to measure cultural variation or generate
culturally varied language.
