Promises and pitfalls of using LLMs to identify actor stances in political discourse.

ABSTRACT: Empirical research in the social sciences is often interested in understanding actor stances; the positions that social actors take regarding normative statements in societal discourse. In automated text analysis applications, the classification task of stance detection remains challenging. Stance detection is especially difficult due to semantic challenges such as implicitness or missing context but also due to the general nature of the task. In this paper, we explore the potential of Large Language Models (LLMs) to enable stance detection in a generalized (non-domain, non-statement specific) form. Specifically, we test a variety of different general prompt chains for zero-shot stance classifications. Our evaluation data consists of textual data from a real-world empirical research projec

SUBMITTER: Walker V 

PROVIDER: S-EPMC12629487 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature