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Detecting Calls to Action in Multimodal Content: Analysis of the 2021 German Federal Election Campaign on Instagram

arXiv 2409.02690 ↗First posted 4 Sep 2024cs.SI cs.CLcited by 2

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Abstract (arXiv)

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.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 7 of its 40 references that are in the map. First posted on arXiv 4 Sep 2024.

Builds on

7 of 40 references.

References · 7

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  2. 2020Language Models are Few-Shot Learnerscited by 32,419
  3. 2023ChatGPT Outperforms Crowd-Workers for Text-Annotation Taskscited by 870
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  6. 2024Best Practices for Text Annotation with Large Language Modelscited by 36
  7. 2021Exploiting the relationship between visual and textual features in social networks for image classification with zero-shot deep learningcited by 2

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 1; 2025: 0; 2026: 1.

2 papers.