Conspiracy Frame: a Semiotically-Driven Approach for Conspiracy Theories Detection
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arXiv
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| Auteurs principaux: | , , , , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866912977862524928 |
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| author | Piva, Heidi Campana Ashraf, Shaina Jouneghani, Maziar Kianimoghadam Longo, Arianna Damiano, Rossana Flek, Lucie Stranisci, Marco Antonio |
| author_facet | Piva, Heidi Campana Ashraf, Shaina Jouneghani, Maziar Kianimoghadam Longo, Arianna Damiano, Rossana Flek, Lucie Stranisci, Marco Antonio |
| contents | 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 semantically- and semiotically-aware detection of conspiratorial narratives. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_21368 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Conspiracy Frame: a Semiotically-Driven Approach for Conspiracy Theories Detection Piva, Heidi Campana Ashraf, Shaina Jouneghani, Maziar Kianimoghadam Longo, Arianna Damiano, Rossana Flek, Lucie Stranisci, Marco Antonio Computation and Language 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 semantically- and semiotically-aware detection of conspiratorial narratives. |
| title | Conspiracy Frame: a Semiotically-Driven Approach for Conspiracy Theories Detection |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2603.21368 |