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

Autor:	Achmann-Denkler, Michael, Fehle, Jakob, Haim, Mario, Wolff, Christian
Rok vydání:	2024
Předmět:	Computer Science - Social and Information Networks Computer Science - Computation and Language
Druh dokumentu:	Working Paper
Popis:	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. Comment: Accepted Archival Paper for the CPSS Workshop at KONVENS 2024. Camera Ready Submission
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2409.02690 Zobrazit plný text záznamu View this record from Arxiv