arXiv AI
Jul 17
Large language models can effectively convince people to believe conspiracies
arXiv:2601. 05050v3 Announce Type: replace Abstract: Large language models (LLMs) have been shown to be persuasive across a variety of contexts.
Reducing belief in conspiracy theories as they unfold using large language models
arXiv:2608. 06151v1 Announce Type: cross Abstract: The emergence of conspiracy theories in the wake of major events is a significant societal challenge.
Related stories
arXiv Computation and Language
Sep 7
Do Androids Dream of Unseen Puppeteers? Probing for a Conspiracy Tendencies in Large Language Models
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 Computation and Language
Sep 22
Who's Behind It? Annotating and Extracting Conspiratorial Actors from German Telegram Posts
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 Computation and Language
Sep 15
A Multi-Stage Agentic Framework for Effective Counter-Narrative Generation and Refinement
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...
arXiv Machine Learning
Sep 25
Agentic Detection of Online Conspiracies
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 AI
Jul 29
Evaluating Communicative Belief Updates in Large Language Models via Implicature Recognition and Cancellation
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.
arXiv Computation and Language
Sep 2
Whether LLMs Can Navigate Beliefs and Facts Depends on How You Phrase It
arXiv:2608.17809v2 Announce Type: replace Abstract: Humans naturally form and express beliefs in daily communication, e.g., "I think the answer is 3" or "I suppose that's right." Such beliefs inevita...
arXiv Computation and Language
Aug 31
Beyond the Rabbit Hole: Mapping the Relational Harms of QAnon Radicalization
The paper examines the relational harms of QAnon radicalization by analyzing 12,747 stories from the r/QAnonCasualties support group. Using a computational pipeline, the authors extract thematic traits, cluster them into six radicalization personas, and link these personas to specific emotional harms through LLM-assisted emotion detection and regression modeling. The study finds that certain personas predict distinct emotional outcomes, such as anger and disgust for ideologically driven radicalization, and fear and sadness for personal and cognitive collapse.
arXiv AI
Aug 3
The persuasive power of large language models does not depend on their perceived national origin
arXiv:2607. 29334v1 Announce Type: cross Abstract: Conversational AI developed by geopolitical rivals reaches citizens worldwide, raising concerns that it could sway public opinion or be rejected as foreign propaganda, with consequences for democratic discourse and information sovereignty.
arXiv AI
3d ago
Stress-Testing LLM Lie Detectors: Role-Play Failures and Spurious Correlations
The paper examines the reliability of lie detection probes for language models when the models adopt anti-factual personas, such as conspiracy theorists. A dataset of 8,916 human-reviewed responses from three LLMs was created, and eight existing probes were evaluated, revealing many fail to flag falsehoods under these personas. The authors also constructed confounder datasets showing that probes often track spurious correlations like instruction compliance, and propose a simple linear probe that performs best on both persona and confounder tests.
arXiv Computation and Language
Sep 25
Benchmarking Argumentative Behaviour of LLMs: A Study of Defences Against Character Attacks
The paper investigates how well large language models (LLMs) can handle character attacks—ad hominem arguments—in political debates. By analyzing natural political dialogues and comparing LLM-generated responses to a corpus of U.S. presidential debates, the study finds that most LLMs favor logical defenses and rarely use ethos-based counterattacks. The authors suggest that safety fine‑tuning limits LLMs’ strategic options, preventing them from fully engaging in realistic political discourse.
arXiv AI
Aug 25
Interrupting the Chain: Human Perception of AI-Generated Disinformation Through a Kill Chain Lens
arXiv:2608.21389v1 Announce Type: cross Abstract: Generative AI enables customized misinformation at scale, yet defenses remain largely reactive. We present empirical findings from a human-subject st...