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Achmann-Denkler, Michael ; Fehle, Jakob ; Haim, Mario ; Wolff, Christian

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

Achmann-Denkler, Michael , Fehle, Jakob, Haim, Mario and Wolff, Christian (2024) Detecting Calls to Action in Multimodal Content: Analysis of the 2021 German Federal Election Campaign on Instagram. arXiv, 2409.02690.

Date of publication of this fulltext: 22 Oct 2024 09:21
Article
DOI to cite this document: 10.5283/epub.59409


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 ...

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.



Involved Institutions


Details

Item typeArticle
Journal or Publication TitlearXiv
Page Range:2409.02690
Date4 September 2024
InstitutionsLanguages and Literatures > Institut für Information und Medien, Sprache und Kultur (I:IMSK) > Lehrstuhl für Medieninformatik (Prof. Dr. Christian Wolff)
Informatics and Data Science > Department Human-Centered Computing > Lehrstuhl für Medieninformatik (Prof. Dr. Christian Wolff)
Identification Number
ValueType
2409.02690arXiv ID
10.48550/arXiv.2409.02690DOI
KeywordsSocial and Information Networks, Computation and Language
Dewey Decimal Classification000 Computer science, information & general works > 004 Computer science
StatusPublished
RefereedYes, this version has been refereed
Created at the University of RegensburgYes
URN of the UB Regensburgurn:nbn:de:bvb:355-epub-594098
Item ID59409

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