  In this paper, we propose a deep, globally normalized topic model that
incorporates structural relationships connecting documents in socially
generated corpora, such as online forums. Our model (1) captures discursive
interactions along observed reply links in addition to traditional topic
information, and (2) incorporates latent distributed representations arranged
in a deep architecture, which enables a GPU-based mean-field inference
procedure that scales efficiently to large data. We apply our model to a new
social media dataset consisting of 13M comments mined from the popular internet
forum Reddit, a domain that poses significant challenges to models that do not
account for relationships connecting user comments. We evaluate against
existing methods across multiple metrics including perplexity and metadata
prediction, and qualitatively analyze the learned interaction patterns.
