A conceptual framework for ideology beyond the left and right

TL;DR

This paper proposes a comprehensive framework for understanding ideology as a complex, multi-level socio-cognitive network, moving beyond simple left/right classifications to better analyze social discourse.

Contribution

It introduces a novel conceptual framework that links NLP analysis of discourse with detailed ideology theory, enabling richer and more nuanced social analysis.

Findings

Clarifies overlaps between stance detection and inference tasks

Reveals new research directions in NLP and social theory

Bridges computational methods with ideology theory

Abstract

NLP+CSS work has operationalized ideology almost exclusively on a left/right partisan axis. This approach obscures the fact that people hold interpretations of many different complex and more specific ideologies on issues like race, climate, and gender. We introduce a framework that understands ideology as an attributed, multi-level socio-cognitive concept network, and explains how ideology manifests in discourse in relation to other relevant social processes like framing. We demonstrate how this framework can clarifies overlaps between existing NLP tasks (e.g. stance detection and natural language inference) and also how it reveals new research directions. Our work provides a unique and important bridge between computational methods and ideology theory, enabling richer analysis of social discourse in a way that benefits both fields.