The Structural Topic Model and Applied Social Science

Year of Conference


Conference Proceedings

We develop the Structural Topic Model which provides a general way to incorporate corpus structure or document metadata into the standard topic model. Document-level covariates enter the model through a simple generalized linear model framework in the prior distributions controlling either topical prevalence or topical content. We demonstrate the model’s use in two applied problems: the analysis of open-ended responses in a survey experiment about immigration policy, and understanding differing media coverage of China’s rise.

Conference Name
Advances in Neural Information Processing Systems Workshop on Topic Models: Computation, Application, and Evaluation
Conference Location
Lake Tahoe, Nevada

Peer-Reviewed Conference Workshop. Selected for Oral Presentation.


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