Understanding how social power structures affect the way we interact with one another is of great interest to social scientists who want to answer fundamental questions about human behavior, as well as to computer scientists who want to build automatic methods to infer the social contexts of interactions. In this paper, we employ advancements in extra-propositional semantics extraction within NLP to study how author commitment reflects the social context of an interaction. Specifically, we investigate whether the level of commitment expressed by individuals in an organizational interaction reflects the hierarchical power structures they are part of. We find that subordinates use significantly more instances of non-commitment than superiors. More importantly, we also find that subordinates attribute propositions to other agents more often than superiors do --- an aspect that has not been studied before. Finally, we show that enriching lexical features with commitment labels captures important distinctions in social meanings.
We study the perception of situational power in written dialogs in the context of organizational emails and contrast it to the power attributed by organizational hierarchy. We analyze various correlates of the perception of power in the dialog structure and language use by participants in the dialog. We also present an SVM-based machine learning system using dialog structure and lexical features to predict persons with situational power in a given communication thread. In this paper we explore the written dialog behavior of participants in anon line discussion for automatic identification of participants who pursue power within the discussion group. We employ various standard unsupervised machine learning approaches to make this prediction. Our approach relies on the identification of certain discourse structures and linguistic techniques used by participants in the discussion. We achive an F-measure of 69.5 using unsupervised methods. We introduce the problem of predicting who has power over whom in pairs of people based on a single written dialog. We propose a new set of structural features. We build a supervised learning system to predict the direction of power; our new features significantly improve the results over using previously proposed features.