A conceptual framework for ideology beyond the left and right

2026-03-19

AI Summary

This work addresses the common oversimplification in natural language processing (NLP) and computational social science that reduces ideology to a unidimensional left–right political spectrum, thereby neglecting its multifaceted structure across issues such as race, climate, and gender. To overcome this limitation, the paper proposes a multilayered socio-cognitive conceptual network framework that models ideology as an attributable, dynamic system of social cognition, analyzed through framing mechanisms in discourse. By integrating NLP, computational social science, and ideological theory, this approach transcends traditional single-axis models and offers a unified perspective linking tasks like stance detection and natural language inference. The framework enables finer-grained analysis of ideology in social discourse and fosters bidirectional synergy between NLP methodologies and social theory.

Technology Category

• Natural Language Processing: Sentiment Analysis, Stylistic Analysis, and Argument Mining
• Cognitive Modeling & Cognitive Systems: Social Cognition And Interaction
• Application Domains: Humanities & Computational Social Science

Application Category

• Social Networks and Social Media: Computational social science
• Economics, Online Markets and Human Computation: Social networks and social learning
• Web Mining and Content Analysis: Sentiment analysis and opinion mining

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.

Problem

• ideology
• left/right axis
• discourse analysis
• socio-cognitive framework
• computational social science

Innovation

• ideology
• socio-cognitive framework
• discourse analysis
• computational social science
• stance detection