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

• URL: http://arxiv.org/abs/2603.21368v1
• Date: Sun, 22 Mar 2026 18:59:59 GMT
• Title: Conspiracy Frame: a Semiotically-Driven Approach for Conspiracy Theories Detection
• Abstract summary: We introduce the Conspiracy Frame: a fine-grained semantic representation of conspiratorial narratives derived from frame-semantics and semiotics.<n>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.
• Score: 8.81667917459116
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• Who's Behind It? Annotating and Extracting Conspiratorial Actors from German Telegram Posts [0.13048920509133807]
  We develop guidelines for conspiratorial actors, present a span-annotated corpus of German Telegram posts, and investigate their automatic extraction.<n>Our results demonstrate that conspiratorial actors can be annotated with meaningful agreement and extracted with reasonable accuracy.
  arXiv  Detail & Related papers  (2026-07-06T11:49:32Z)
• Codified Foreshadowing-Payoff Text Generation [67.01182739162142]
  Foreshadowing and payoff are ubiquitous narrative devices through which authors introduce commitments early in a story and resolve them through concrete, observable outcomes.<n>Existing evaluations largely overlook this structural failure, focusing on surface-level coherence rather than the logical fulfillment of narrative setups.<n>We introduce Codified Foreshadowing-Payoff Generation, a novel framework that reframes narrative quality through the lens of payoff realization.
  arXiv  Detail & Related papers  (2026-01-11T19:05:37Z)
• ConspirED: A Dataset for Cognitive Traits of Conspiracy Theories and Large Language Model Safety [87.90209836101353]
  ConspirED is the first dataset of conspiratorial content annotated for general cognitive traits.<n>We develop computational models that identify conspiratorial traits and determine dominant traits in text excerpts.<n>We evaluate large language/reasoning model (LLM/LRM) robustness to conspiratorial inputs.
  arXiv  Detail & Related papers  (2025-08-28T06:39:25Z)
• Narrative Media Framing in Political Discourse [11.38723572165938]
  Narrative frames are a powerful way of conceptualizing and communicating complex, controversial ideas.<n>In this paper, we connect elements of narrativity with fundamental aspects of framing, and present a framework which formalizes and operationalizes such aspects.
  arXiv  Detail & Related papers  (2025-05-31T22:55:08Z)
• Seeing Through Deception: Uncovering Misleading Creator Intent in Multimodal News with Vision-Language Models [65.23999399834638]
  We introduce DeceptionDecoded, a benchmark of 12,000 image-caption pairs grounded in trustworthy reference articles.<n>The dataset captures both misleading and non-misleading cases, spanning manipulations across visual and textual modalities.<n>It supports three intent-centric tasks: misleading intent detection, misleading source attribution, and creator desire inference.
  arXiv  Detail & Related papers  (2025-05-21T13:14:32Z)
• Talking Point based Ideological Discourse Analysis in News Events [62.18747509565779]
  We propose a framework motivated by the theory of ideological discourse analysis to analyze news articles related to real-world events.<n>Our framework represents the news articles using a relational structure - talking points, which captures the interaction between entities, their roles, and media frames along with a topic of discussion.<n>We evaluate our framework's ability to generate these perspectives through automated tasks - ideology and partisan classification tasks, supplemented by human validation.
  arXiv  Detail & Related papers  (2025-04-10T02:52:34Z)
• Classifying Conspiratorial Narratives At Scale: False Alarms and Erroneous Connections [4.594855794205588]
  This work establishes a general scheme for classifying discussions related to conspiracy theories. We leverage human-labeled ground truth to train a BERT-based model for classifying online CTs. We present the first large-scale classification study using posts from the most active conspiracy-related Reddit forums.
  arXiv  Detail & Related papers  (2024-03-29T20:29:12Z)
• Codes, Patterns and Shapes of Contemporary Online Antisemitism and Conspiracy Narratives -- an Annotation Guide and Labeled German-Language Dataset in the Context of COVID-19 [0.0]
  Antisemitic and conspiracy theory content on the Internet makes data-driven algorithmic approaches essential. We develop an annotation guide for antisemitic and conspiracy theory online content in the context of the COVID-19 pandemic. We provide working definitions, including specific forms of antisemitism such as encoded and post-Holocaust antisemitism.
  arXiv  Detail & Related papers  (2022-10-13T10:32:39Z)
• Latent Topology Induction for Understanding Contextualized Representations [84.7918739062235]
  We study the representation space of contextualized embeddings and gain insight into the hidden topology of large language models. We show there exists a network of latent states that summarize linguistic properties of contextualized representations.
  arXiv  Detail & Related papers  (2022-06-03T11:22:48Z)
• An automated pipeline for the discovery of conspiracy and conspiracy theory narrative frameworks: Bridgegate, Pizzagate and storytelling on the web [0.0]
  We present an automated pipeline for the discovery and description of the generative narrative frameworks of conspiracy theories on social media. We base this work on two separate repositories of posts and news articles describing the well-known conspiracy theory Pizzagate from 2016. We show how the Pizzagate framework relies on the conspiracy theorists' interpretation of "hidden knowledge" to link otherwise unlinked domains of human interaction.
  arXiv  Detail & Related papers  (2020-08-23T05:14:38Z)
• SF-Net: Single-Frame Supervision for Temporal Action Localization [60.202516362976645]
  Single-frame supervision introduces extra temporal action signals while maintaining low annotation overhead. We propose a unified system called SF-Net to make use of such single-frame supervision. SF-Net significantly improves upon state-of-the-art weakly-supervised methods in terms of both segment localization and single-frame localization.
  arXiv  Detail & Related papers  (2020-03-15T15:06:01Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.