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
PhD Candidate | NLP Researcher
Hi! My name is Maziar, though you can call me Maaz. I’m currently a PhD candidate in Computational Sciences and Technological Innovation, specializing in Artificial Intelligence and Data Science, at the DISIT department, University of Eastern Piedmont (UPO). I’m part of the ARC2S group, working under the supervision of Prof. Giancarlo Ruffo and Prof. Anna Sapienza. I finished my Master’s in Language Technologies and Digital Humanities at the University of Turin in July 2026.
My research over the past two years has centered on multilingual and cross-cultural NLP, and on harmful online content, specifically how it spreads and how it is perceived differently across cultures and communities.
This interest comes from my background. I grew up bilingual in Persian and Qashqai Turkish (a Turkic language of the Oghuz branch), and then did my bachelor’s in English Language Translation, with a focus on linguistics and translation studies. This is the starting point of my interest in meaning, cultures, and languages. We didn’t have interdisciplinary NLP master’s program in my country that I could join with that background (and we still don’t!), so I applied abroad and came to Italy to study NLP.
My master’s thesis, supervised by Prof. Viviana Patti and Dr. Marco Antonio Stranisci, builds on a community-developed dataset on information disorder, InDor. The idea was that an LLM’s fluent reasoning about manipulative content is not necessarily culturally aligned. So I thought maybe retrieving items and explanations from native speakers based on semantic similarity (instead of static few-shot) could fill this gap a bit. I built this retrieval framework and tested it on Persian and Italian, with automatic metrics, a qualitative analysis, and a human evaluation study.
In general, my research interests are: Multilingual & Cross-Cultural NLP, Computational Social Science (CSS), Explainable AI, Fairness, In-Context Learning, Human-in-the-Loop AI, and Information Disorder.
Beyond academia, I spent over 3 years in SEO, web content, and digital marketing.
My research over the past two years has centered on multilingual and cross-cultural NLP, and on harmful online content, specifically how it spreads and how it is perceived differently across cultures and communities.
This interest comes from my background. I grew up bilingual in Persian and Qashqai Turkish (a Turkic language of the Oghuz branch), and then did my bachelor’s in English Language Translation, with a focus on linguistics and translation studies. This is the starting point of my interest in meaning, cultures, and languages. We didn’t have interdisciplinary NLP master’s program in my country that I could join with that background (and we still don’t!), so I applied abroad and came to Italy to study NLP.
My master’s thesis, supervised by Prof. Viviana Patti and Dr. Marco Antonio Stranisci, builds on a community-developed dataset on information disorder, InDor. The idea was that an LLM’s fluent reasoning about manipulative content is not necessarily culturally aligned. So I thought maybe retrieving items and explanations from native speakers based on semantic similarity (instead of static few-shot) could fill this gap a bit. I built this retrieval framework and tested it on Persian and Italian, with automatic metrics, a qualitative analysis, and a human evaluation study.
In general, my research interests are: Multilingual & Cross-Cultural NLP, Computational Social Science (CSS), Explainable AI, Fairness, In-Context Learning, Human-in-the-Loop AI, and Information Disorder.
Beyond academia, I spent over 3 years in SEO, web content, and digital marketing.