Rumor susceptibility and intervention strategies of large language model-driven social agents

YIN Yongjie; YUAN Jingwei; GONG Qingyuan; CHEN Yang

doi:10.11959/j.issn.2096-6652.202545

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Rumor susceptibility and intervention strategies of large language model-driven social agents

Topics\：Social Computing and Resilient Society | 更新时间：2026-03-18

- Rumor susceptibility and intervention strategies of large language model-driven social agents
- Chinese Journal of Intelligent Science and Technology Vol. 7, Issue 4, Pages: 517-532(2025)
- 作者机构：
  
  1.复旦大学计算与智能创新学院，上海 200438
  2.复旦大学智能复杂体系基础理论与关键技术实验室，上海 200438
- 作者简介：
- 基金信息：
  
  The National Natural Science Foundation of China(62102094)
- DOI：10.11959/j.issn.2096-6652.202545
  CLC： TP39
- Received：30 September 2025，
  
  Revised：2025-12-15，
  
  Accepted：21 December 2025，
  
  Published：15 December 2025
- 稿件说明：
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殷勇杰,袁靖炜,宫庆媛等.大模型驱动的社交智能体谣言易感性与干预策略研究[J].智能科学与技术学报,2025,07(04):517-532.

YIN Yongjie,YUAN Jingwei,GONG Qingyuan,et al.Rumor susceptibility and intervention strategies of large language model-driven social agents[J].Chinese Journal of Intelligent Science and Technology,2025,07(04):517-532.
殷勇杰,袁靖炜,宫庆媛等.大模型驱动的社交智能体谣言易感性与干预策略研究[J].智能科学与技术学报,2025,07(04):517-532. DOI： 10.11959/j.issn.2096-6652.202545.

YIN Yongjie,YUAN Jingwei,GONG Qingyuan,et al.Rumor susceptibility and intervention strategies of large language model-driven social agents[J].Chinese Journal of Intelligent Science and Technology,2025,07(04):517-532. DOI： 10.11959/j.issn.2096-6652.202545.

摘要

近年来，社交平台上涌现出众多大语言模型（large language model，LLM）驱动的社交智能体，其深度参与到用户的在线社交活动中。然而，社交平台的内容并不都是经过验证的真实信息，智能体的信息传播能力可能加剧谣言传播。衡量智能体对谣言的易感性、降低其对谣言的采信程度是亟待解决的问题。为了应对该风险，系统地探究了智能体在社交场景下对谣言的易感性与观点演变轨迹。研究结果表明，智能体不仅对未知谣言高度易感，其观点也会随着长期接触谣言而逐渐强化。此外，智能体在参与社交活动时，具有明显的采信谣言倾向。为缓解智能体对谣言的易感性，提出了“自提示”干预策略。该策略可以使智能体对谣言的采信率从75.91%显著降至13.50%，并有效促使持有中立观点的智能体转向反谣言立场。所提策略不仅揭示了LLM驱动的智能体的谣言易感机制，也为增强其抗谣言能力提供了可行路径，保障了LLM智能体在社交平台上安全部署，降低智能体带来的谣言传播风险。

Abstract

In recent years

numerous social agents empowered by large language model (LLM) have emerged on social media

which play a significant role in online social interactions. However

since not all messages shared on social media are verified as genuine

the involvement of these agents could amplify rumor propagation. Therefore

it becomes increasingly important to measure agents' susceptibility to rumors and reduce their acceptance of rumors. To tackle this issue

agents' susceptibility to rumors and how their opinion evolve on social media were systematically examined. The findings demonstrate that agents are highly susceptible to unknown rumors

tend to reinforce their beliefs over time through prolonged exposure. Furthermore

agents show a strong tendency to believe rumors during social interactions. To reduce agents' susceptibility to rumors

a “self-prompting” intervention strategy was proposed

which significantly reduced rumor acceptance rate among agents from 75.91% to 13.50% and effectively motivated agents with a neutral stance to take on anti-rumor positions. This research not only deepens our understanding of the mechanisms behind the rumor susceptibility of LLM-driven agents

but also provides an effective pathway to improve their anti-rumor capabilities

thereby offering support for the safe deployment of agents on social media and reduction of rumor propagation.

关键词

Keywords

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