  Can language analysis reveal the underlying social power relations that exist
between participants of an interaction? Prior work within NLP has shown promise
in this area, but the performance of automatically predicting power relations
using NLP analysis of social interactions remains wanting. In this paper, we
present a novel neural architecture that captures manifestations of power
within individual emails which are then aggregated in an order-preserving way
in order to infer the direction of power between pairs of participants in an
email thread. We obtain an accuracy of 80.4%, a 10.1% improvement over
state-of-the-art methods, in this task. We further apply our model to the task
of predicting power relations between individuals based on the entire set of
messages exchanged between them; here also, our model significantly outperforms
the70.0% accuracy using prior state-of-the-art techniques, obtaining an
accuracy of 83.0%.
