检测多模态内容中的行动号召:分析 Instagram 上的 2021 年德国联邦大选活动

Michael Achmann-Denkler, Jakob Fehle, Mario Haim, Christian Wolff
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引用次数: 0

摘要

本研究调查了 2021 年德国 Instagram 选举活动中行动号召(CTA)的自动分类,以促进对社交媒体语境下动员的理解。我们使用微调 BERT 模型和 OpenAI 的 GPT-4 模型分析了超过 2,208 个 Instagram 故事和 712 个帖子。结合合成训练数据的微调 BERT 模型获得了 0.93 的宏观 F1 分数,显示出强大的分类性能。我们的分析表明,49.58% 的 Instagram 帖子和 10.64% 的故事包含 CTA,这突显了这些内容类型在动员策略上的显著差异。此外,我们发现 FDP 和绿党在帖子中使用 CTA 的比例最高,而基民盟和基社盟在故事中使用 CTA 的比例最高。
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Detecting Calls to Action in Multimodal Content: Analysis of the 2021 German Federal Election Campaign on Instagram
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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