@inproceedings{achmann-denkler-etal-2024-detecting,
title = "Detecting Calls to Action in Multimodal Content: Analysis of the 2021 {G}erman Federal Election Campaign on {I}nstagram",
author = "Achmann-Denkler, Michael and
Fehle, Jakob and
Haim, Mario and
Wolff, Christian",
editor = "Klamm, Christopher and
Lapesa, Gabriella and
Ponzetto, Simone Paolo and
Rehbein, Ines and
Sen, Indira",
booktitle = "Proceedings of the 4th Workshop on Computational Linguistics for the Political and Social Sciences: Long and short papers",
month = sep,
year = "2024",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.cpss-1.1",
pages = "1--13",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T Detecting Calls to Action in Multimodal Content: Analysis of the 2021 German Federal Election Campaign on Instagram
%A Achmann-Denkler, Michael
%A Fehle, Jakob
%A Haim, Mario
%A Wolff, Christian
%Y Klamm, Christopher
%Y Lapesa, Gabriella
%Y Ponzetto, Simone Paolo
%Y Rehbein, Ines
%Y Sen, Indira
%S Proceedings of the 4th Workshop on Computational Linguistics for the Political and Social Sciences: Long and short papers
%D 2024
%8 September
%I Association for Computational Linguistics
%C Vienna, Austria
%F achmann-denkler-etal-2024-detecting
%X 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.
%U https://aclanthology.org/2024.cpss-1.1
%P 1-13
Markdown (Informal)
[Detecting Calls to Action in Multimodal Content: Analysis of the 2021 German Federal Election Campaign on Instagram](https://aclanthology.org/2024.cpss-1.1) (Achmann-Denkler et al., cpss-WS 2024)
ACL