Large Language Models Unpack Complex Political Opinions through Target-Stance Extraction
Political polarization emerges from a complex interplay of beliefs about policies, figures, and issues. However, most computational analyses reduce discourse to coarse partisan labels, overlooking how these beliefs interact. This is especially evident in online political conversations, which are often nuanced and cover a wide range of subjects, making it difficult to automatically identify the target of discussion and the opinion expressed toward them. In this study, we investigate whether Large Language Models (LLMs) can address this challenge through Target-Stance Extraction (TSE), a recent natural language processing task that combines target identification and stance detection, enabli...
| Authors | Özgür Togay, Javier Garcia-Bernardo, Florian Kunneman et al. |
|---|---|
| Primary category | cs.CL |
| Published | 7 March 2026 |
| Last updated at source | 20 May 2026 |
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