Disinformation campaigns can distort public perception and destabilize institutions. Understanding how different populations respond to information is crucial for designing effective interventions, yet real-world experimentation is impractical and ethically challenging. To address this, we develop an agent-based simulation using Large Language Models (LLMs) to model responses to misinformation. We construct agent personas spanning five professions and three mental schemas, and evaluate their reactions to news headlines. Our findings show that LLM-generated agents align closely with ground-truth labels and human predictions, supporting their use as proxies for studying information responses. We also find that mental schemas, more than professional background, influence how agents interpret misinformation. This work provides a validation of LLMs to be used as agents in an agent-based model of an information network for analyzing trust, polarization, and susceptibility to deceptive content in complex social systems.
Large Language Models in Misinformation Ecosystems: Misuse, Defense, and Vulnerability
Institute of Information Engineering, Chinese Academy of Sciences（中国科学院信息工程研究所）；University of Illinois Chicago（伊利诺伊大学香槟分校）；State Key Laboratory of Media Convergence and Communication, Communication University of China（中国传媒大学媒体融合与传播国家重点实验室）；University of Chinese Academy of Sciences（中国科学院大学）
OFFSIDE: Benchmarking Unlearning Misinformation in Multimodal Large Language Models
Harbin Institute of Technology（哈尔滨工业大学）；University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；BIAI, ZJUT & D5Data.ai（BIAI、浙江工业大学及D5Data.ai）；National University of Singapore（新加坡国立大学）
Deceptive AI systems that give explanations are more convincing than honest AI systems and can amplify belief in misinformation
Massachusetts Institute of Technology（麻省理工学院）；MIT Media Lab（麻省理工学院媒体实验室）；Kellogg School of Management（凯洛格管理学院）；Northwestern University（西北大学）；Institute for Human-Centered AI（以人为中心的人工智能研究所）；Stanford University（斯坦福大学）
Combating Misinformation in the Arab World: Challenges & Opportunities
New York University Abu Dhabi（纽约大学阿布扎赫尔分校）；Qatar Computing Research Institute（卡塔尔计算研究所）；College of Science and Engineering, Hamad Bin Khalifa University（哈马德·本·卡西姆大学科学与工程学院）；EURECOM Biot France（EURECOM法国生物研究中心）
ConspirED: A Dataset for Cognitive Traits of Conspiracy Theories and Large Language Model Safety
Technical University of Darmstadt（达姆施塔特工业大学）；National Research Center for Applied Cybersecurity ATHENE（国家应用网络安全研究中心ATHENE）；Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）；Walmart Inc.（沃尔玛公司）
Susceptibility of Large Language Models to User-Driven Factors in Medical Queries
Yonsei University College of Medicine（延世大学医学院）；Yonsei University（延世大学）；Massachusetts General Hospital（麻省总医院）；Harvard Medical School（哈佛医学院）；Oncosoft Inc.（Oncosoft公司）
Chatbots to strengthen democracy: An interdisciplinary seminar to train identifying argumentation techniques of science denial
Mobile Dialog Systems, Otto-von-Guericke-University Magdeburg, Germany（移动对话系统，马格德堡奥托·冯·格里克大学）；German Research Centre for Artificial Intelligence, Berlin（德国人工智能研究中心）；Usability Lab, TU Berlin, Germany（可用性实验室，柏林技术大学）
In Vino Veritas and Vulnerabilities: Examining LLM Safety via Drunk Language Inducement
School of Computer Science and Engineering, UNSW Sydney（计算机科学与工程学院，新南威尔士大学悉尼分校）；School of Computing and Information System, the University of Melbourne（计算与信息系统学院，墨尔本大学）
Large Language Model-driven Security Assistant for Internet of Things via Chain-of-Thought
Guangxi Power Grid Co., Ltd.（广西电网公司）；School of Cyberspace Science and Technology, Beijing Institute of Technology（北京理工大学信息空间科学与技术学院）；Guangxi Engineering Research Center of Industrial Internet Security and Blockchain, Guilin University of Electronic Technology（桂林电子科技大学工业互联网安全与区块链工程研究中心）
Accidental Vulnerability: Factors in Fine-Tuning that Shift Model Safeguards
University of Toronto（多伦多大学）；Vector Institute（向量研究所）；ETH Zürich（苏黎世联邦理工学院）；FAR AI（FAR人工智能公司）；McGill University（麦吉尔大学）；MILA（蒙特利尔人工智能研究院）；Max Planck Institute for Intelligent Systems, Tübingen, Germany（图宾根德国智能系统研究所）
Knowledge-Based Design Requirements for Generative Social Robots in Higher Education
Zurich University of Applied Sciences（应用科学大学苏黎世）；University of Applied Sciences and Arts Northwestern Switzerland（西北瑞士应用科学与艺术大学）；Bielefeld University（比勒菲尔德大学）
Nanyang Technological University（南洋理工大学）；AI Singapore（新加坡人工智能研究院）；Hangzhou Innovation Institute, Beihang University（北京航空航天大学杭州创新研究院）；School of Computer Science and Technology, Anhui University（安徽大学计算机科学与技术学院）；Qiyuan laboratory（启元实验室）；IHPC, Agency for Science, Technology and Research, Singapore（新加坡科技研究局高性能计算研究院）；CoAI Group, Tsinghua University（清华大学CoAI组）；Institute of High Performance Computing, Agency for Science, Technology and Research (A*STAR), Singapore（新加坡科技研究局高性能计算研究院）
