  Word embeddings provide an unsupervised way to understand differences in word
usage between discursive communities. A number of recent papers have focused on
identifying words that are used differently by two or more communities. But
word embeddings are complex, high-dimensional spaces and a focus on identifying
differences only captures a fraction of their richness. Here, we take a step
towards leveraging the richness of the full embedding space, by using word
embeddings to map out how words are used differently. Specifically, we describe
the construction of dialectograms, an unsupervised way to visually explore the
characteristic ways in which each community use a focal word. Based on these
dialectograms, we provide a new measure of the degree to which words are used
differently that overcomes the tendency for existing measures to pick out low
frequent or polysemous words. We apply our methods to explore the discourses of
two US political subreddits and show how our methods identify stark affective
polarisation of politicians and political entities, differences in the
assessment of proper political action as well as disagreement about whether
certain issues require political intervention at all.
