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

TL;DR

This paper presents a study on automatically detecting Calls to Action in German Instagram election campaign content, using advanced NLP models to analyze mobilization strategies and content differences.

Contribution

It introduces a novel approach combining fine-tuned BERT and GPT-4 models for CTA detection in multimodal social media content, with high accuracy.

Findings

Nearly 50% of posts contained CTAs, compared to 10.6% of stories.

Different political parties used CTAs with varying prevalence in posts and stories.

The models achieved a macro F1 score of 0.93 for CTA classification.

Abstract

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.