  Online social media have become an integral part of our social beings.
Analyzing conversations in social media platforms can lead to complex
probabilistic models to understand social interaction networks. In this paper,
we present a modeling approach for characterizing social interaction networks
by jointly inferring user communities and interests based on social media
interactions. We present several pattern inference models: i) Interest pattern
model (IPM) captures population level interaction topics, ii) User interest
pattern model (UIPM) captures user specific interaction topics, and iii)
Community interest pattern model (CIPM) captures both community structures and
user interests. We test our methods on Twitter data collected from Purdue
University community. From our model results, we observe the interaction topics
and communities related to two big events within Purdue University community,
namely Purdue Day of Giving and Senator Bernie Sanders' visit to Purdue
University as part of Indiana Primary Election 2016. Constructing social
interaction networks based on user interactions accounts for the similarity of
users' interactions on various topics of interest and indicates their community
belonging further beyond connectivity. We observed that the
degree-distributions of such networks follow power-law that is indicative of
the existence of fewer nodes in the network with higher levels of interactions,
and many other nodes with less interactions. We also discuss the application of
such networks as a useful tool to effectively disseminate specific information
to the target audience towards planning any large-scale events and demonstrate
how to single out specific nodes in a given community by running network
algorithms.
