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

arXiv: https://arxiv.org/abs/2607.04962 · HTML: https://mlsanity.com/paper/2607.04962/

## 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.

## Builds on (most cited first, 10 of 10)

- [An automated pipeline for the discovery of conspiracy and conspiracy theory narrative frameworks: Bridgegate, Pizzagate and storytelling on the web](https://mlsanity.com/paper/2008.09961/) (2020)
- [Probing and Fine-tuning Reading Comprehension Models for Few-shot Event Extraction](https://mlsanity.com/paper/2010.11325/) (2020)
- [Conspiracy theories and where to find them on TikTok](https://mlsanity.com/paper/2407.12545/) (2024)
- [More than Memes: A Multimodal Topic Modeling Approach to Conspiracy Theories on Telegram](https://mlsanity.com/paper/2410.08642/) (2024)
- [A Study on Agreement in PICO Span Annotations](https://mlsanity.com/paper/1904.09557/) (2019)
- [Codes, Patterns and Shapes of Contemporary Online Antisemitism and Conspiracy Narratives -- an Annotation Guide and Labeled German-Language Dataset in the Context of COVID-19](https://mlsanity.com/paper/2210.07934/) (2022)
- [Detection of Conspiracy Theories Beyond Keyword Bias in German-Language Telegram Using Large Language Models](https://mlsanity.com/paper/2404.17985/) (2024)
- [ACTI at EVALITA 2023: Overview of the Conspiracy Theory Identification Task](https://mlsanity.com/paper/2307.06954/) (2023)
- [ConspirED: A Dataset for Cognitive Traits of Conspiracy Theories and Large Language Model Safety](https://mlsanity.com/paper/2508.20468/) (2025)
- [Conspiracy Frame: a Semiotically-Driven Approach for Conspiracy Theories Detection](https://mlsanity.com/paper/2603.21368/) (2026)

## 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.