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

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 extrapropositional semantics extraction within NLP to study how author commitment reflects the social context of an interactions. 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.

A General Framework for Domain-Specialization of Stance Detection

“…The belief strength scale aligns with Factbank (Saurí and Pustejovsky 2009): certain (+3.0), probable (+2.0), possible (+1.0), uncertain (0.0), unlikely (-1.0), improbable (-2.0), impossible (-3.0). These values are drawn from a small domain-independent lexicon adapted from our prior work on committed belief (Prabhakaran, Ganeshkumar, and Rambow 2018), and modality and negation (Baker et al 2012). Sentiment is derived through composition of lexical terms from a small sentiment lexicon of general positive/negative terms (e.g., like, hate; see (Levin 1993)), negation terms, and domain-specific terms such as protect (inherently positive) and restrict (inherently negative).…”

The anatomy and ontology of organizational power as a fractal metaphor: A philosophical approach

“…So far formal "organizational power relations" have studied from different aspects, e.g. gender relations (Chernyak-Hai & Waismel-Manor, 2019;Hearn & Collinson, 2018), organizational interaction (Prabhakaran, Ganeshkumar, & Rambow, 2018), interpersonal influence (Lovrić, Lovrić, & Schraml, 2019), educational perspective (Horton, 2018), etc. On the contrary, informal power has usually hidden and invisible existence; e.g.…”

Uncertainty to Avoid Entrapment: Comparing Internet Stings to Real Victim Conversations

“…The main area of study for uncertainty in NLP is in assessment of uncertainty expressed in research articles [12]. However, uncertainty and commitment is useful in understanding social constructs and phenomena such as expressions of social power [14], relationship between speakers [9], and deception [5] in informal text as well.…”