  People are observed to assortatively connect on a set of traits. This
phenomenon, termed assortative mixing or sometimes homophily, can be quantified
through assortativity coefficient in social networks. Uncovering the exact
causes of strong assortative mixing found in social networks has been a
research challenge. Among the main suggested causes from sociology are the
tendency of similar individuals to connect (often itself referred as homophily)
and the social influence among already connected individuals. An important
question to researchers and in practice can be tackled, as we present here:
understanding the exact mechanisms of interplay between these tendencies and
the underlying social network structure. Namely, in addition to the mentioned
assortativity coefficient, there are several other static and temporal network
properties and substructures that can be linked to the tendencies of homophily
and social influence in the social network and we herein investigate those.
Concretely, we tackle a computer-mediated \textit{communication network} (based
on Twitter mentions) and a particular type of assortative mixing that can be
inferred from the semantic features of communication content that we term
\textit{semantic homophily}. Our work, to the best of our knowledge, is the
first to offer an in-depth analysis on semantic homophily in a communication
network and the interplay between them. We quantify diverse levels of semantic
homophily, identify the semantic aspects that are the drivers of observed
homophily, show insights in its temporal evolution and finally, we present its
intricate interplay with the communication network on Twitter. By analyzing
these mechanisms we increase understanding on what are the semantic aspects
that shape and how they shape the human computer-mediated communication.
