arXiv:2608. 06151v1 Announce Type: cross Abstract: The emergence of conspiracy theories in the wake of major events is a significant societal challenge.
Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.
arXiv:2601. 05050v3 Announce Type: replace Abstract: Large language models (LLMs) have been shown to be persuasive across a variety of contexts.
The paper examines whether large language models (LLMs) exhibit conspiratorial tendencies, socio-demographic biases in this domain, and how easily they can be conditioned to adopt conspiratorial viewpoints. Using validated psychometric surveys, the authors find that LLMs partially align with conspiracy beliefs, that conditioning with demographic attributes yields uneven effects revealing latent biases, and that targeted prompts can readily shift responses toward conspiratorial stances. These findings underscore the vulnerability of LLMs to manipulation and the potential risks of deploying them in sensitive contexts.
arXiv:2607.04962v2 Announce Type: replace Abstract: Conspiracy theories commonly attribute important events to the actions of powerful and secretive actors. While computational research has largely f...
arXiv:2609.14178v1 Announce Type: new Abstract: The rapid diffusion of hate speech and misinformation on social networks challenges democratic societies, since direct suppression efforts may deepen p...
The paper presents an agentic framework for detecting conspiratorial content in social media by inferring the speaker’s intent rather than merely identifying explicit claims. It leverages social context and adaptive tool use, demonstrating superior performance over text-only and non-agentic models on a large Hebrew tweet dataset spanning election cycles and the COVID pandemic. The study highlights the importance of context-aware, reasoning-driven approaches for accurate conspiracy detection.
arXiv:2607. 25094v1 Announce Type: cross Abstract: Human language is driven by unspoken beliefs and belief updates, making these critical to model for successful communication between large language models (LLMs) and their users.