Stance Detection: A Practical Guide to Classifying Political Beliefs in Text

TL;DR

This paper provides a comprehensive guide to stance detection in political text, introducing three approaches—supervised, natural language inference, and in-context learning—and offering practical guidance for implementation and validation.

Contribution

It advances stance detection by defining it clearly and presenting three distinct, practical approaches with detailed guidance and tutorials for each.

Findings

Natural language inference can effectively classify stance.

In-context learning with language models replicates supervised classifiers.

Trade-offs between resources and accuracy are discussed for each method.

Abstract

Stance detection is identifying expressed beliefs in a document. While researchers widely use sentiment analysis for this, recent research demonstrates that sentiment and stance are distinct. This paper advances text analysis methods by precisely defining stance detection and presenting three distinct approaches: supervised classification, natural language inference, and in-context learning with generative language models. I discuss how document context and trade-offs between resources and workload should inform your methods. For all three approaches I provide guidance on application and validation techniques, as well as coding tutorials for implementation. Finally, I demonstrate how newer classification approaches can replicate supervised classifiers.