  Decades of social science research identified ten fundamental dimensions that
provide the conceptual building blocks to describe the nature of human
relationships. Yet, it is not clear to what extent these concepts are expressed
in everyday language and what role they have in shaping observable dynamics of
social interactions. After annotating conversational text through
crowdsourcing, we trained NLP tools to detect the presence of these types of
interaction from conversations, and applied them to 160M messages written by
geo-referenced Reddit users, 290k emails from the Enron corpus and 300k lines
of dialogue from movie scripts. We show that social dimensions can be predicted
purely from conversations with an AUC up to 0.98, and that the combination of
the predicted dimensions suggests both the types of relationships people
entertain (conflict vs. support) and the types of real-world communities
(wealthy vs. deprived) they shape.
