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
[UNDER REVIEW] — Preprint available on arXiv
Authors
NLP Researcher
Hi! I’m an NLP and Computational Linguistics researcher with a Master’s degree in Language Technologies and Digital Humanities from the University of Turin, where I graduated in July 2026. I’m interested in the intersection of language, culture, and AI.
My master’s thesis, supervised by Prof. Viviana Patti and Dr. Marco Antonio Stranisci, grew out of a community-developed dataset on information disorder (InDor). It proposed a retrieval-based framework to connect LLMs’ reasoning to native-speaker explanations from InDor, based on semantic similarity, in order to align LLMs better on assessing and reasoning about manipulation signals across different cultures and languages. This framework was tested on Italian and Persian, using automatic metrics, qualitative analysis, and a human evaluation study.
My Bachelor’s degree is in English language translation, focused on linguistics and translation studies.
Also, beyond academia, I spent over 3 years in SEO, web content, and digital marketing.
My research interests are: Multilingual & Cross-Cultural NLP, Computational Social Science (CSS), Explainable AI, In-Context Learning, Human-in-the-Loop AI, and Information Disorder.
My master’s thesis, supervised by Prof. Viviana Patti and Dr. Marco Antonio Stranisci, grew out of a community-developed dataset on information disorder (InDor). It proposed a retrieval-based framework to connect LLMs’ reasoning to native-speaker explanations from InDor, based on semantic similarity, in order to align LLMs better on assessing and reasoning about manipulation signals across different cultures and languages. This framework was tested on Italian and Persian, using automatic metrics, qualitative analysis, and a human evaluation study.
My Bachelor’s degree is in English language translation, focused on linguistics and translation studies.
Also, beyond academia, I spent over 3 years in SEO, web content, and digital marketing.
My research interests are: Multilingual & Cross-Cultural NLP, Computational Social Science (CSS), Explainable AI, In-Context Learning, Human-in-the-Loop AI, and Information Disorder.