  Topic models analyze text from a set of documents. Documents are modeled as a
mixture of topics, with topics defined as probability distributions on words.
Inferences of interest include the most probable topics and characterization of
a topic by inspecting the topic's highest probability words. Motivated by a
data set of web pages (documents) nested in web sites, we extend the Poisson
factor analysis topic model to hierarchical topic presence models for analyzing
text from documents nested in known groups. We incorporate an unknown binary
topic presence parameter for each topic at the web site and/or the web page
level to allow web sites and/or web pages to be sparse mixtures of topics and
we propose logistic regression modeling of topic presence conditional on web
site covariates. We introduce local topics into the Poisson factor analysis
framework, where each web site has a local topic not found in other web sites.
Two data augmentation methods, the Chinese table distribution and
P\'{o}lya-Gamma augmentation, aid in constructing our sampler. We analyze text
from web pages nested in United States local public health department web sites
to abstract topical information and understand national patterns in topic
presence.
