Author Commitment and Social Power: Automatic Belief Tagging to Infer the Social Context of Interactions
This work provides insights for social scientists and computer scientists by enabling automatic inference of social contexts, though it is incremental as it builds on existing NLP methods for a new application.
The paper tackled the problem of inferring social power structures from organizational interactions by analyzing author commitment in language, finding that subordinates use significantly more non-commitment and attribute propositions to others more often than superiors.
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.