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相关论文: Persuasion at Play: Understanding Misinformation D…

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Large language models (LLMs) make it possible to generate synthetic behavioural data at scale, offering an ethical and low-cost alternative to human experiments. Whether such data can faithfully capture psychological differences driven by…

计算与语言 · 计算机科学 2025-11-27 Manuel Pratelli , Marinella Petrocchi

Misinformation is a growing societal threat, and susceptibility to misinformative claims varies across demographic groups due to differences in underlying beliefs. As Large Language Models (LLMs) are increasingly used to simulate human…

计算与语言 · 计算机科学 2026-05-27 Angana Borah , Zohaib Khan , Rada Mihalcea , Verónica Pérez-Rosas

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…

社会与信息网络 · 计算机科学 2025-11-10 David Farr , Lynnette Hui Xian Ng , Stephen Prochaska , Iain J. Cruickshank , Jevin West

Social biases and belief-driven behaviors can significantly impact Large Language Models (LLMs) decisions on several tasks. As LLMs are increasingly used in multi-agent systems for societal simulations, their ability to model fundamental…

计算与语言 · 计算机科学 2025-10-09 Angana Borah , Marwa Houalla , Rada Mihalcea

Large language models (LLMs) have demonstrated remarkable capabilities in simulating human behaviour and social intelligence. However, they risk perpetuating societal biases, especially when demographic information is involved. We introduce…

计算机与社会 · 计算机科学 2025-06-11 Bryan Chen Zhengyu Tan , Roy Ka-Wei Lee

Warning: This research studies AI persuasion and bias amplification that could be misused; all experiments are for safety evaluation. Large Language Models (LLMs) now generate convincing, human-like text and are widely used in content…

计算与语言 · 计算机科学 2025-08-25 Saumya Roy

Large Language Models (LLMs) are becoming increasingly persuasive, demonstrating the ability to personalize arguments in conversation with humans by leveraging their personal data. This may have serious impacts on the scale and…

计算与语言 · 计算机科学 2025-01-30 Jasper Timm , Chetan Talele , Jacob Haimes

Large language models (LLMs) are increasingly used as proxies for human judgment in computational social science, yet their ability to reproduce patterns of susceptibility to misinformation remains unclear. We test whether LLM-simulated…

社会与信息网络 · 计算机科学 2026-04-13 Eun Cheol Choi , Lindsay E. Young , Emilio Ferrara

Misinformation on social media thrives on surprise, emotion, and identity-driven reasoning, often amplified through human cognitive biases. To investigate these mechanisms, we model large language model (LLM) personas as synthetic agents…

社会与信息网络 · 计算机科学 2025-12-10 Raj Gaurav Maurya , Vaibhav Shukla , Raj Abhijit Dandekar , Rajat Dandekar , Sreedath Panat

Large Language Models (LLMs) have shown remarkable capabilities in knowledge-intensive tasks, while they remain vulnerable when encountering misinformation. Existing studies have explored the role of LLMs in combating misinformation, but…

计算与语言 · 计算机科学 2025-05-29 Miao Peng , Nuo Chen , Jianheng Tang , Jia Li

The proliferation of fake news in the digital age has raised critical concerns, particularly regarding its impact on societal trust and democratic processes. Diverging from conventional agent-based simulation approaches, this work…

社会与信息网络 · 计算机科学 2024-10-21 Xinyi Li , Yu Xu , Yongfeng Zhang , Edward C. Malthouse

Large Language Models (LLMs) have emerged as formidable instruments capable of comprehending and producing human-like text. This paper explores the potential of LLMs, to shape user perspectives and subsequently influence their decisions on…

人工智能 · 计算机科学 2024-09-04 Ganesh Prasath Ramani , Shirish Karande , Santhosh V , Yash Bhatia

Large Language Models (LLMs) can generate content that is as persuasive as human-written text and appear capable of selectively producing deceptive outputs. These capabilities raise concerns about potential misuse and unintended…

计算与语言 · 计算机科学 2024-12-24 Cameron R. Jones , Benjamin K. Bergen

Misinformation such as fake news and rumors is a serious threat on information ecosystems and public trust. The emergence of Large Language Models (LLMs) has great potential to reshape the landscape of combating misinformation. Generally,…

计算机与社会 · 计算机科学 2023-11-13 Canyu Chen , Kai Shu

Large Language Models (LLMs) are increasingly regarded as having the potential to generate persuasive content at scale. While previous studies have focused on the risks associated with LLM-generated misinformation, the role of LLMs in…

计算机与社会 · 计算机科学 2026-04-06 John Caffier , Olga Stavrova , Bennett Kleinberg

Large language models (LLMs) encapsulate vast amounts of knowledge but still remain vulnerable to external misinformation. Existing research mainly studied this susceptibility behavior in a single-turn setting. However, belief can change…

计算与语言 · 计算机科学 2024-06-03 Rongwu Xu , Brian S. Lin , Shujian Yang , Tianqi Zhang , Weiyan Shi , Tianwei Zhang , Zhixuan Fang , Wei Xu , Han Qiu

Large language model (LLM) agents are increasingly acting as human delegates in multi-agent environments, where a representative agent integrates diverse peer perspectives to make a final decision. Drawing inspiration from social…

计算与语言 · 计算机科学 2026-05-05 Changgeon Ko , Jisu Shin , Hoyun Song , Huije Lee , Eui Jun Hwang , Jong C. Park

The emergence of Large Language Models (LLMs) presents a dual challenge in the fight against disinformation. These powerful tools, capable of generating human-like text at scale, can be weaponised to produce sophisticated and persuasive…

Recent advances in Large Language Models (LLMs) have enabled multi-agent systems that simulate real-world interactions with near-human reasoning. While previous studies have extensively examined biases related to protected attributes such…

人工智能 · 计算机科学 2025-06-03 Min Choi , Keonwoo Kim , Sungwon Chae , Sangyeob Baek

Misinformation is on the rise, and the strong writing capabilities of LLMs lower the barrier for malicious actors to produce and disseminate false information. We study how LLMs behave when prompted to spread misinformation across languages…

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