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...
By Helena Mihaljevi\'c, Jolanda Beer, Mareike Lisker, Katharina Soemer
arXiv:2608. 06151v1 Announce Type: cross Abstract: The emergence of conspiracy theories in the wake of major events is a significant societal challenge.
By Thomas H. Costello, Nathaniel Rabb, Michael Nicholas Stagnaro, Gordon Pennycook, David Rand
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.
By Francesco Corso, Francesco Pierri, Gianmarco De Francisci Morales
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...
By Carmel Kronfeld, Sharva Gogawale, Tetsuro Kobayashi, Irad Ben-Gal
arXiv:2607. 02734v1 Announce Type: cross Abstract: Rapid growth in social media has transformed global communication by enabling fast information exchange, but it has also accelerated the spread of misinformation.
By Md. Maruf Bangabashi, Tahmid Hasan, Golam Mahmud, Md. Mostafijur Rahman, Md. Toufiqur Rahman, Jahanur Biswas
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.
By Lior Biton, Oren Tsur