Power Networks: A Novel Neural Architecture to Predict Power Relations
This work addresses the challenge of automatically inferring power dynamics in social interactions for NLP applications, representing a strong specific gain rather than a broad paradigm shift.
The paper tackles the problem of predicting social power relations from language in email interactions, achieving an accuracy of 80.4% for pairwise predictions and 83.0% for aggregated messages, with improvements of 10.1% and 13.0% over prior state-of-the-art methods, respectively.
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%.