  Vast amounts of human communication occurs online. These digital traces of
natural human communication along with recent advances in natural language
processing technology provide for computational analysis of these discussions.
In the study of social networks the typical perspective is to view users as
nodes and concepts as flowing through and among the user-nodes within the
social network. In the present work we take the opposite perspective: we
extract and organize massive amounts of group discussion into a concept space
we call an entity graph where concepts and entities are static and human
communicators move about the concept space via their conversations. Framed by
this perspective we performed several experiments and comparative analysis on
large volumes of online discourse from Reddit. In quantitative experiments, we
found that discourse was difficult to predict, especially as the conversation
carried on. We also developed an interactive tool to visually inspect
conversation trails over the entity graph; although they were difficult to
predict, we found that conversations, in general, tended to diverge to a vast
swath of topics initially, but then tended to converge to simple and popular
concepts as the conversation progressed. An application of the spreading
activation function from the field of cognitive psychology also provided
compelling visual narratives from the data.
