A conceptual framework for ideology beyond the left and right
This work provides a bridge between computational methods and ideology theory, enabling richer analysis of social discourse for researchers in NLP and social sciences.
The paper tackles the limitation of representing ideology solely on a left-right axis in NLP and CSS, proposing a multi-level conceptual framework that views ideology as a socio-cognitive network; it demonstrates how this framework clarifies overlaps between existing tasks like stance detection and reveals new research directions.
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.