Posts Talk Policy, Stories Don’t: Policy-Issue Detection on Instagram with Fine-Tuned Transformers and Prompted LLMs
Proceedings of the Second Workshop on Building Educational Applications Using NLP
Abstract
Policy issues are central to election campaigns, yet systematic analyses of issue communication on Instagram remain scarce — particularly for ephemeral Stories. We develop and evaluate automated methods for detecting the binary presence of policy issues in Instagram posts and Stories from the 2021 German federal election. Drawing on a gold-standard dataset of 1,357 annotated documents across three textual channels (captions, OCR-extracted image text, and speech transcripts), we compare a fine-tuned German transformer (GBERT) with multiple LLM prompting strategies (zero-shot, few-shot, retrieval-augmented). Both approaches prove effective: GBERT achieves a cross-validated macro F1 of 0.90, closely matched by GPT-o3 under few-shot prompting (0.88). Substantively, policy visibility varies far more by content format than by party: 70% of posts contain policy references compared to only 17% of Stories, a pattern that holds consistently across all eight parties. An exploratory topic model confirms that parties reproduce familiar issue-ownership profiles within the subset of policy-relevant texts. Our results establish binary issue detection as a feasible foundation for studying policy communication in multimodal, ephemeral social media environments.