mlsanityData to 14 Oct 2026

Who's Behind It? Annotating and Extracting Conspiratorial Actors from German Telegram Posts

arXiv 2607.04962 ↗First posted 6 Jul 2026cs.CLcited by 0

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Abstract (arXiv)

Conspiracy theories commonly attribute important events to the actions of powerful and secretive actors. While computational research has largely focused on document-level analyses of conspiracy theories, less attention has been paid to identifying the actors that drive such narratives. We develop annotation guidelines for conspiratorial actors, present a span-annotated corpus of German Telegram posts, and investigate their automatic extraction using transformer-based models. We further apply the resulting model to the textitSchwurbelarchiv, a large-scale archive of German conspiracy-related Telegram channels. Our results demonstrate that conspiratorial actors can be annotated with meaningful agreement and extracted with reasonable accuracy despite the linguistic complexity of conspiracy discourse, enabling large-scale analyses of actor representations in conspiracy narratives.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 10 of its 61 references that are in the map. First posted on arXiv 6 Jul 2026.

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10 of 61 references.

References · 10

  1. 2020An automated pipeline for the discovery of conspiracy and conspiracy theory narrative frameworks: Bridgegate, Pizzagate and storytelling on the webcited by 16
  2. 2020Probing and Fine-tuning Reading Comprehension Models for Few-shot Event Extractioncited by 11
  3. 2024Conspiracy theories and where to find them on TikTokcited by 7
  4. 2024More than Memes: A Multimodal Topic Modeling Approach to Conspiracy Theories on Telegramcited by 7
  5. 2019A Study on Agreement in PICO Span Annotationscited by 6
  6. 2022Codes, Patterns and Shapes of Contemporary Online Antisemitism and Conspiracy Narratives -- an Annotation Guide and Labeled German-Language Dataset in the Context of COVID-19cited by 6
  7. 2024Detection of Conspiracy Theories Beyond Keyword Bias in German-Language Telegram Using Large Language Modelscited by 4
  8. 2023ACTI at EVALITA 2023: Overview of the Conspiracy Theory Identification Taskcited by 2
  9. 2025ConspirED: A Dataset for Cognitive Traits of Conspiracy Theories and Large Language Model Safetycited by 1
  10. 2026Conspiracy Frame: a Semiotically-Driven Approach for Conspiracy Theories Detectioncited by 1

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