Conspiracy Frame: a Semiotically-Driven Approach for Conspiracy Theories Detection

2026-03-22

AI Summary

This study addresses the disruptive influence of conspiracy narratives on public perception of political information, owing to their anti-authoritarian and socially inflammatory nature. To model such narratives with fine-grained precision, this work proposes a generalizable “conspiracy frame” by integrating frame semantics and semiotics for the first time. The authors also introduce Con.Fra., the first span-level annotated dataset of Telegram messages dedicated to conspiracy discourse. Leveraging in-context learning with large language models and FrameNet-based mapping analysis, the study evaluates model performance in both in-domain and out-of-domain conspiracy detection. Although incorporating semantic frames does not yield significant performance gains, the findings illuminate a novel detection pathway that combines semantic structures with symbolic features.

Technology Category

• Natural Language Processing: Fact-Checking / Misinformation Detection (NLP Focus)
• Application Domains: Humanities & Computational Social Science
• Machine Learning: Neuro-Symbolic Learning

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Responsible Web: Mitigating misinformation and disinformation
• Web Mining and Content Analysis: Large pretrained models with web data

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 semantically- and semiotically-aware detection of conspiratorial narratives.

Problem

• conspiracy theories
• frame semantics
• semiotics
• narrative detection
• social conflict

Innovation

• Conspiracy Frame
• frame semantics
• semiotics
• span-level annotation
• conspiratorial narratives