Who's Behind It? Annotating and Extracting Conspiratorial Actors from German Telegram Posts
2026-07-06 • Computation and Language
Computation and Language
AI summaryⓘ
The authors studied how conspiracy theories often blame secret and powerful people for big events. They created rules to label mentions of these actors in German Telegram messages and made a dataset showing where these mentions appear. They used advanced computer models to automatically find these actors in texts and tested their method on a large collection of conspiracy messages. Their work shows it is possible to reliably identify actors in conspiracy stories, which helps analyze who is blamed across many messages.
conspiracy theoriesactorsannotation guidelinesGerman Telegramtransformer modelsnatural language processingtext corpusinformation extractionSchwurbelarchivnamed entity recognition
Authors
Helena Mihaljević, Jolanda Beer, Mareike Lisker, Katharina Soemer
Abstract
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 \textit{Schwurbelarchiv}, 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.