{"type":"ScholarlyArticle","title":"From Skepticism to Acceptance: Simulating the Attitude Dynamics Toward Fake News","authors":[{"name":"Yuhan Liu","slug":"yuhan-liu","given":"Yuhan","family":"Liu","affiliation":"Gaoling School of Artificial Intelligence , Renmin University of China"},{"name":"Xiuying Chen","slug":"xiuying-chen","given":"Xiuying","family":"Chen","affiliation":"Mohamed bin Zayed University of Artificial Intelligence"},{"name":"Xiaoqing Zhang","slug":"xiaoqing-zhang","given":"Xiaoqing","family":"Zhang","affiliation":"Gaoling School of Artificial Intelligence , Renmin University of China"},{"name":"Xing Gao","slug":"xing-gao","given":"Xing","family":"Gao","affiliation":"Alibaba DAMO Academy"},{"name":"Ji Zhang","slug":"ji-zhang","given":"Ji","family":"Zhang","affiliation":"Alibaba DAMO Academy"},{"name":"Rui Yan","slug":"rui-yan","given":"Rui","family":"Yan","affiliation":"Gaoling School of Artificial Intelligence , Renmin University of China"}],"abstract":"In the digital era, the rapid propagation of fake news and rumors via social networks brings notable societal challenges and impacts public opinion regulation. Traditional fake news modeling typically forecasts the general popularity trends of different groups or numerically represents opinions shift. However, these methods often oversimplify real-world complexities and overlook the rich semantic information of news text. The advent of large language models (LLMs) provides the possibility of modeling subtle dynamics of opinion. Consequently, in this work, we introduce a Fake news Propagation Simulation framework (FPS) based on LLM, which studies the trends and control of fake news propagation in detail. Specifically, each agent in the simulation represents an individual with a distinct personality.  They are equipped with both short-term and long-term memory, as well as a reflective mechanism to mimic human-like thinking. Every day, they engage in random opinion exchanges, reflect on their thinking, and update their opinions. Our simulation results uncover patterns in fake news propagation related to topic relevance, and individual traits, aligning with real-world observations. Additionally, we evaluate various intervention strategies and demonstrate that early and appropriately frequent interventions strike a balance between governance cost and effectiveness, offering valuable insights for practical applications. Our study underscores the significant utility and potential of LLMs in combating fake news.","doi":"10.24963/ijcai.2024/873","url":"https://ijcai.org/proceedings/2024/873","pdf":"https://ijcai.org/proceedings/2024/0873.pdf","markdown":"https://ijcai.org/proceedings/2024/873.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/873","volume":{"slug":"2024","title":"Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2024","url":"https://ijcai.org/proceedings/2024/","year":2024,"location":"Jeju, Korea","dates":"3-9 August 2024","editors":"Kate Larson","isbn":"978-1-956792-04-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2024"},"section":"Human-Centred AI","track":"Human-Centred AI","number":873,"pages":{"first":7886,"last":7894},"keywords":["Multidisciplinary Topics and Applications: MTA: News and media","Multidisciplinary Topics and Applications: MTA: Social sciences","Humans and AI: HAI: Applications","Natural Language Processing: NLP: Applications"],"videos":[],"references":[{"position":1,"raw":"[Afassinou, 2014] Komi Afassinou. 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