Conspiracy Frame: a Semiotically-Driven Approach for Conspiracy Theories Detection
Mar 20, 2026·,
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0 min read
Heidi Campana Piva
Shaina Ashraf
Maziar Kianimoghadam Jouneghani
Arianna Longo
Rossana Damiano
Lucie Flek
Marco Antonio Stranisci
Abstract
Conspiracy theories are anti-authoritarian narratives that lead to social conflict, impacting how people perceive political information. To help in understanding this issue, we introduce the Conspiracy Frame: a fine-grained semantic representation of conspiratorial narratives derived from frame-semantics and semiotics, which spawned the Conspiracy Frames (Con.Fra.) dataset: a corpus of Telegram messages annotated at span-level. The Conspiracy Frame and Con.Fra. dataset contribute to the implementation of a more generalizable understanding and recognition of conspiracy theories. We observe the ability of LLMs to recognize this phenomenon in-domain and out-of-domain, investigating the role that frames may have in supporting this task. Results show that, while the injection of frames in an in-context approach does not lead to clear increase of performance, it has potential; the mapping of annotated spans with FrameNet shows abstract semantic patterns (e.g., ‘Kinship’, ‘Ingest_substance’) that potentially pave the way for a more semanticallyand semiotically-aware detection of conspiratorial narratives.
Type
Publication
[WORKING PAPER] — Preprint available on arXiv
Authors
NLP Researcher
I am an NLP and Computational Linguistics researcher currently pursuing my Master’s degree in Language Technologies and Digital Humanities at University of Turin. My academic focus is on Large Language Models (LLMs), Explainable AI (XAI), multilingual NLP, and Human-in-the-Loop AI (HITL).
Beyond academia, I have over 3 years of hands-on industry experience in SEO, web content, and data-driven digital marketing.
Research Interests:
- Explainable AI (XAI)
- In-Context Learning (ICL)
- Human-in-the-Loop AI (HITL)
- Information Disorder
- Multilingual & Cross-Cultural NLP