用多模态建模分析德语电报群中的阴谋论内容
More than Memes: A Multimodal Topic Modeling Approach to Conspiracy Theories on Telegram
- 结合文本与视觉数据，用BERTopic和CLIP做多模态主题建模
- 在约4万条消息中发现文本与图像主题的重叠与对称性
- 提出分析阴谋论话语策略的框架，适合社交媒体研究者
为应对社交媒体上（音频）视觉内容的日益增多，以及其传播的动态性，研究人员开始探索无监督方法分析多模态在线内容。然而，现有研究常忽略图片之外的视觉内容，且缺乏跨模态主题模型比较方法。本研究通过多模态主题建模分析德语电报群中的阴谋论，使用BERTopic与CLIP对2023年10月来自571个涉密电报群的约4万条消息进行文本与视觉数据分析。通过该数据集，我们揭示了单模态与多模态主题模型间的对称性与交集关系，展示了主题建模所发现的文本与视觉内容多样性，并提出一个用于分析阴谋论传播中文字与视觉话语策略的概念框架。案例研究聚焦‘以色列-加沙’话题组。
原文摘要 · Abstract (English)
To address the increasing prevalence of (audio-)visual data on social media, and to capture the evolving and dynamic nature of this communication, researchers have begun to explore the potential of unsupervised approaches for analyzing multimodal online content. However, existing research often neglects visual content beyond memes, and in addition lacks methods to compare topic models across modalities. Our study addresses these gaps by applying multimodal topic modeling for analyzing conspiracy theories in German-language Telegram channels. We use BERTopic with CLIP for the analysis of textual and visual data in a corpus of ~40, 000 Telegram messages posted in October 2023 in 571 German-language Telegram channels known for disseminating conspiracy theories. Through this dataset, we provide insights into unimodal and multimodal topic models by analyzing symmetry and intersections of topics across modalities. We demonstrate the variety of textual and visual content shared in the channels discovered through the topic modeling, and propose a conceptual framework for the analysis of textual and visual discursive strategies in the communication of conspiracy theories. We apply the framework in a case study of the topic group Israel Gaza.
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