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<p>Conspiracy theories (CTs) pose significant societal challenges due to their rapid online spread and harmful real-world consequences. Most computational research relies on binary, text-based classification, which helps estimate the prevalence of conspiracist content but reveals little about how CTs are communicated and largely ignores the multimodal nature of online discourse. This study addresses these gaps with a&#xa0;conceptually grounded, scalable multimodal pipeline for fine-grained CT analysis. Using a&#xa0;corpus of German-language conspiracist Telegram messages from November 2024, the pipeline applies zero-shot LLM classification in two stages: binary CT detection and multi-label classification of text- and image-based discursive strategies, such as acts of disclosure, presenting evidence, and discrediting mainstream accounts. We further process the model outputs using lightweight lexical methods to examine strategy frequencies, characteristic terms, and common term co-occurrences, and we qualitatively analyze selected posts to illustrate how discursive strategies manifest at the message level. Our approach achieves solid binary CT classification (F1 = 0.76) and promising fine-grained strategy performance (macro-F1 = 0.51–0.64). Methodologically, we show how LLM-generated labels can be systematically processed for corpus analysis—an aspect often overlooked in prior work. Empirically, we provide insights into German-language CT discourse on Telegram during a&#xa0;politically turbulent period and highlight the role of visual formats such as news-style layouts, collages, and symbolic imagery. To the best of our knowledge, this is the first computational multimodal analysis of CT-related discursive strategies in German-language Telegram channels.</p>

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Conspiracist Collages: a Multimodal LLM-Based Analysis of Discursive Strategies On Telegram

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年2月4日

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  • Elisabeth Steffen

摘要

Conspiracy theories (CTs) pose significant societal challenges due to their rapid online spread and harmful real-world consequences. Most computational research relies on binary, text-based classification, which helps estimate the prevalence of conspiracist content but reveals little about how CTs are communicated and largely ignores the multimodal nature of online discourse. This study addresses these gaps with a conceptually grounded, scalable multimodal pipeline for fine-grained CT analysis. Using a corpus of German-language conspiracist Telegram messages from November 2024, the pipeline applies zero-shot LLM classification in two stages: binary CT detection and multi-label classification of text- and image-based discursive strategies, such as acts of disclosure, presenting evidence, and discrediting mainstream accounts. We further process the model outputs using lightweight lexical methods to examine strategy frequencies, characteristic terms, and common term co-occurrences, and we qualitatively analyze selected posts to illustrate how discursive strategies manifest at the message level. Our approach achieves solid binary CT classification (F1 = 0.76) and promising fine-grained strategy performance (macro-F1 = 0.51–0.64). Methodologically, we show how LLM-generated labels can be systematically processed for corpus analysis—an aspect often overlooked in prior work. Empirically, we provide insights into German-language CT discourse on Telegram during a politically turbulent period and highlight the role of visual formats such as news-style layouts, collages, and symbolic imagery. To the best of our knowledge, this is the first computational multimodal analysis of CT-related discursive strategies in German-language Telegram channels.