Author Commitment and Social Power: Automatic Belief Tagging to Infer the Social Context of Interactions

TL;DR

This paper explores how author commitment levels in organizational interactions reveal social power dynamics, showing subordinates' greater non-commitment and attribution of propositions, with implications for NLP and social science research.

Contribution

It introduces a novel approach using NLP to analyze how commitment reflects social hierarchy, including new findings on attribution patterns and feature enrichment.

Findings

Subordinates use more non-commitment expressions than superiors.

Subordinates attribute propositions to others more frequently.

Enriching features with commitment labels improves social meaning detection.

Abstract

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.