Detecting Calls to Action in Multimodal Content: Analysis of the 2021 German Federal Election Campaign on Instagram
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866916381734207488 |
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| author | Achmann-Denkler, Michael Fehle, Jakob Haim, Mario Wolff, Christian |
| author_facet | Achmann-Denkler, Michael Fehle, Jakob Haim, Mario Wolff, Christian |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_02690 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Detecting Calls to Action in Multimodal Content: Analysis of the 2021 German Federal Election Campaign on Instagram Achmann-Denkler, Michael Fehle, Jakob Haim, Mario Wolff, Christian Social and Information Networks Computation and Language 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. |
| title | Detecting Calls to Action in Multimodal Content: Analysis of the 2021 German Federal Election Campaign on Instagram |
| topic | Social and Information Networks Computation and Language |
| url | https://arxiv.org/abs/2409.02690 |