  The last decade has seen great progress in both dynamic network modeling and
topic modeling. This paper draws upon both areas to create a Bayesian method
that allows topic discovery to inform the latent network model and the network
structure to facilitate topic identification. We apply this method to the 467
top political blogs of 2012. Our results find complex community structure
within this set of blogs, where community membership depends strongly upon the
set of topics in which the blogger is interested.
