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基于连续动作学习的社交机器人舆论引导模型

  • 顾家豪 ,
  • 程纯 ,
  • 丁卫平
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  • 南通大学人工智能与计算机学院,江苏 南通 226019
顾家豪(2001-),男,江苏南通人,硕士研究生,主要研究方向为社交网络建模分析。

收稿日期: 2024-09-11

  修回日期: 2024-10-15

  网络出版日期: 2026-07-14

基金资助

教育部人文社会科学研究青年基金(21YJCZH013);江苏省高校自然科学研究项目-面上项目(22KJB120008)

Public Opinion Guidance Model: Social Bots Based on Continuous Action Learning Method

  • GU Jiahao ,
  • CHENG Chun ,
  • DING Weiping
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  • School of Artificial Intelligence and Computer Science, Nantong University, Nantong 226019, China

Received date: 2024-09-11

  Revised date: 2024-10-15

  Online published: 2026-07-14

摘要

在社交媒体日益成为社会舆论传播的重要平台的背景下,社交机器人对网络公众舆论存在恶意引导和操纵现象。为了探究其引导机制,结合经典Hegselmann-Krause模型与连续动作学习自动机算法,采用多智能体方法对社交机器人与用户个体之间的观点交互进行微观建模,并通过仿真捕获网络舆论的宏观分布特性。实验结果表明,社交机器人在不同复杂的环境中能够有效使用CALA算法获取网络关注度,引导用户群体观点向预定目标方向发展。研究验证了机器人在舆论引导中的可行性。

本文引用格式

顾家豪 , 程纯 , 丁卫平 . 基于连续动作学习的社交机器人舆论引导模型[J]. 复杂系统与复杂性科学, 2026 , 23(3) : 80 -88 . DOI: 10.13306/j.1672-3813.2026.03.010

Abstract

In the context of social media increasingly becoming an important platform for the spread of public opinion, social bots have malicious guidance and manipulation of online public opinion. In order to explore its guidance mechanism, this paper combines the classic Hegselmann-Krause model with the continuous action learning automaton algorithm (CALA), uses a multi-agent method to model the interaction between social bots and individual users, and captures the distribution characteristics of online public opinion through simulation. Experimental results show that social bots can effectively use the CALA algorithm to obtain network attention in different complex environments and guide the opinions of user groups to develop in the direction of predetermined goals. The study verifies the feasibility of bots in public opinion guidance.

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