Calls-to-action (CTAs) are central to digital campaigning, yet computational research has largely focused on binary detection only. We address CTA type classification in German Instagram campaign texts (posts and ephemeral stories), distinguishing Support, Inform, Interact, and No CTA. With limited annotated data, we benchmark a fine-tuned GBERT model against GPT models using zero-shot, few-shot, and retrieval-augmented few-shot prompting in a multi-label setup. Both approaches reach similar performance in five-fold cross-validation (macro-F1 ca. 0.79), with persistent difficulty on the rare Interact category. As a proof of concept, we apply the selected setup to the 2021 federal election corpus and show that parties varied not only in overall CTA use but also in how they balanced appeals across posts versus stories. The results demonstrate the feasibility of CTA type classification with modest data and position retrieval-augmented prompting as a practical alternative to supervised fine-tuning.

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.

Media framing refers to the emphasis on specific aspects of perceived reality to shape how an issue is defined and understood. Its primary purpose is to shape public perceptions often in alignment with the authors’ opinions and stances. However, the interaction between stance and media frame remains largely unexplored. In this work, we apply an interdisciplinary approach to conceptualize and computationally explore this interaction with internet memes on climate change. We curate CLIMATEMEMES, the first dataset of climate-change memes annotated with both stance and media frames, inspired by research in communication science. CLIMATEMEMES includes 1,184 memes sourced from 47 subreddits, enabling analysis of frame prominence over time and communities, and sheds light on the framing preferences of different stance holders. We propose two meme understanding tasks: stance detection and media frame detection. We evaluate LLaVA-NeXT and Molmo in various setups, and report the corresponding results on their LLM backbone. Human captions consistently enhance performance. Synthetic captions and human-corrected OCR also help occasionally. Our findings highlight that VLMs perform well on stance, but struggle on frames, where LLMs outperform VLMs. Finally, we analyze VLMs’ limitations in handling nuanced frames and stance expressions on climate change internet memes.

This study investigates the automated classification of Calls to Action (CTAs) within the 2021 German Instagram election campaign to advance the understanding of mobilization in social media contexts. We analyzed over 2,208 Instagram stories and 712 posts using fine-tuned BERT models and OpenAI’s GPT-4 models. The fine-tuned BERT model incorporating synthetic training data achieved a macro F1 score of 0.93, demonstrating a robust classification performance. Our analysis revealed that 49.58% of Instagram posts and 10.64% of stories contained CTAs, highlighting significant differences in mobilization strategies between these content types. Additionally, we found that FDP and the Greens had the highest prevalence of CTAs in posts, whereas CDU and CSU led in story CTAs.